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Cerebral blood flow and cognitive function in HIV-infected men with sustained suppressed viremia on combination antiretroviral therapy

Su, Tanjaa; Mutsaerts, Henri J.M.M.a; Caan, Matthan W.A.a; Wit, Ferdinand W.N.M.b,c,j; Schouten, Judithb,d; Geurtsen, Gert J.e; Sharp, David J.f; Prins, Mariag; Richard, Edod,h; Portegies, Peterd,i; Reiss, Peterb,c,j; Majoie, Charles B.a on behalf of the AGEhIV Cohort Study

doi: 10.1097/QAD.0000000000001414
EPIDEMIOLOGY AND SOCIAL
Free

Objective: To assess if HIV-infected patients on long-term successful combination antiretroviral therapy show cerebral blood flow (CBF) alterations in comparison with HIV-uninfected, otherwise similar controls. To explore whether such alterations are associated with HIV-associated cognitive impairment and to explore potential determinants of CBF alterations in HIV.

Design: Cross-sectional comparison of CBF in an observational cohort study.

Methods: Clinical, cognitive and MRI data of 100 middle-aged aviremic HIV-infected men on combination antiretroviral therapy and 69 HIV-uninfected controls were collected and compared. From pseudocontinuous arterial spin labeling MRI data, CBF-maps were calculated. The associations of mean gray matter CBF with clinical and cognitive parameters were explored in regression models, followed by a spatial delineation in a voxel-based analysis.

Results: CBF was decreased in HIV-infected patients compared with HIV-uninfected controls (P = 0.02), adjusted for age, ecstasy use and waist circumference. Spatially distinct and independent effects of total gray matter volume and HIV-serostatus on CBF were found. Within the HIV-infected group, decreased CBF was associated with increased triglyceride levels (P = 0.005) and prior clinical AIDS (P = 0.03). No association between CBF and cognitive impairment was found.

Conclusion: Decreased CBF was observed among HIV-infected patients, which was associated with both vascular risk factors as well as with measures of past immune deficiency. These results provide support for increased vascular disease in HIV-infected patients as represented by hemodynamic alteration, but without overt cognitive consequences within the current cohort of patients on long-term successful treatment.

aDepartment of Radiology

bDepartment of Global Health, Academic Medical Center, Amsterdam Institute for Global Health and Development (AIGHD)

cCenter for Infection and Immunity Amsterdam (CINIMA), Division of Infectious Diseases, Department of Internal Medicine

dDepartment of Neurology

eDepartment of Medical Psychology, Academic Medical Center, Amsterdam, The Netherlands

fThe Computational, Cognitive, and Clinical Neuroimaging Laboratory, Department of Medicine, Imperial College London, London, UK

gPublic Health Service Amsterdam, Infectious Diseases Research, Amsterdam

hDepartment of Neurology, Radboud University Medical Centre, Nijmegen

iDepartment of Neurology, Onze Lieve Vrouwe Gasthuis (OLVG Hospital)

jHIV Monitoring Foundation, Amsterdam, The Netherlands.

Correspondence to Prof Dr Charles B. Majoie, MD, PhD, Department of Radiology, Academic Medical Center, Location C1-426, PO Box 22660, 1100 DD Amsterdam, The Netherlands. Tel: +0031 205669111; fax: +0031 205669119; e-mail: c.b.majoie@amc.uva.nl

Received 27 August, 2016

Revised 22 December, 2016

Accepted 10 January, 2017

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Introduction

With the introduction of combination antiretroviral therapy (cART), the incidence of severe HIV-related complications has decreased substantially and life expectancy has been prolonged [1]. Despite these advances, HIV-associated cognitive impairment is commonly reported, even among patients with a systemically well controlled HIV infection by cART [2–4]. The pathogenesis of such HIV-associated cognitive impairment is poorly understood, but the increased cardiovascular risk factors among long-term HIV-infected and treated patients may play a role.

Several factors may contribute to vascular disease in HIV-infected patients, either due to the virus itself or to cART therapy. The HIV-infection may directly affect blood vessels by the release of toxic viral proteins and induce host proinflammatory responses [5]. Although such deleterious effects of HIV can be attenuated by successful suppression of viral replication by cART, immune activation and inflammation may persist [6,7]. In addition, HIV infection may indirectly cause vessel wall damage, due to the use of particular cART regimens [8] and associated metabolic complications including dyslipidemia, insulin resistance and hypertension [9–11]. Furthermore, certain lifestyle risk factors (e.g. smoking, alcohol and recreational drug use) are more prevalent among HIV-infected patients and may further contribute to vascular disease, and as the HIV-infected population ages, vascular disease may further accumulate by age-related risk factors [12].

In HIV-uninfected individuals, white matter hyperintensities (WMH), neuroimaging correlates of cerebrovascular disease, have been associated with cognitive decline, particularly among individuals at increased risk for cardiovascular disease (CVD) [13]. Such WMH have also been reported to be more extensive in HIV and associated with cognitive deficits [14–18]. Cerebrovascular disease and cerebral blood flow (CBF) are assumed to be interrelated, and the extent of WMH burden is associated with CBF decline over time [19].

To date, a few studies have examined CBF in HIV-infected patients and their results vary widely [20–23]. Reports on associations between CBF alterations and cognitive impairment are contradictory as well [21,24]. It thus remains unclear how CBF is affected in HIV-infected patients with suppressed viremia on cART, and if an association with cognitive impairment exists. Therefore, we have compared a group of middle-aged HIV-infected men with long-term and successful cART with HIV-uninfected controls with similar sociodemographic backgrounds and lifestyles to investigate potential associations between CBF and HIV status and to assess whether CBF is associated with HIV-associated cognitive impairment. Furthermore, potential determinants of CBF alterations in HIV were explored.

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Methods

Study population

The current study is nested within the AGEhIV cohort study. This is an ongoing study on the prevalence, incidence and risk factors of age-associated comorbidities in HIV-infected patients and comparable HIV-uninfected controls (e.g. same sociodemographic background and lifestyle), aged 45 years or older [25]. Eligible male participants from this cohort were approached to participate in a nested neuroimaging substudy. For the HIV-infected patients, sustained suppressed viremia on cART (plasma HIV-RNA < 40 copies/ml) for at least 12 months was required (transient low-level viremia, i.e. 40–200 copies/ml was allowed). Exclusion criteria were current or past neurological disorders including stroke, seizure disorder, multiple sclerosis, (HIV-associated) dementia and traumatic brain injury (defined as loss of consciousness >30 min), (HIV-associated) central nervous system infection or tumor, current severe psychiatric disorders (e.g. psychosis and major depression), injecting drug use, daily use of noninjecting illicit drugs (daily cannabis use was permissible), excessive alcohol consumption (>48 units of alcohol/week), insufficient command of the Dutch language, mental retardation and MRI contraindications.

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Standard protocol approval, registrations and patient consents

The AGEhIV cohort study and the nested neuroimaging substudy were both approved by the institutional review board of the Academic Medical Center and are registered at www.clinicaltrials.gov (identifier: NCT01466582). From all participants, written informed consent was obtained separately for the cohort study and the nested neuroimaging study.

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Clinical parameters

Participants completed standardized questionnaires on demographics, medical characteristics and lifestyle factors. Standardized screenings for age-associated comorbidity and organ dysfunction were performed, and blood and urine samples were collected for laboratory testing. Information on HIV and cART history was obtained. Detailed description of these procedures was published previously [25].

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Cognitive parameters

A full neuropsychological assessment was performed, covering six cognitive domains (fluency, attention, processing speed, memory, executive function and motor function). Test scores were adjusted for age and education effects using normative standards. Multivariate normative comparison (MNC) was performed to identify cognitive impairment, as this method has been shown to be a more reliable method for detecting cognitive impairment than the Frascati criteria [26]. The MNC method is designed to control the false positive rate while retaining sensitivity [27]. It identifies HIV-infected patients as cognitively impaired if their cognitive profile deviates significantly from those of the HIV-uninfected control group, based on the Hotelling's T2 statistic. This statistic was used to create a continuous measure to reflect cognitive function. In addition, the Global Deficit Score (GDS) [28] was computed. Details on the test battery and MNC method were published previously [26,29].

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MRI protocol

MRI was performed on two Philips 3T scanners (Intera and Ingenia, Philips Healthcare; Best, The Netherlands) due to a scanner replacement midway through the study. The number of patients and controls scanned on the two systems was comparable (Table 1). All patients were requested to abstain from nicotine (≥2 h), caffeine-containing beverages (≥5 h) and noninjecting illicit drugs (≥14 days) prior to the MRI examination to minimize physiological CBF fluctuations.

Table 1

Table 1

The scanning protocol consisted of pseudocontinuous arterial spin labeling (ASL) for measuring CBF [echo time (TE)/repetition time (TR) = 14/4000 ms, 240 × 240 mm2 field of view (FOV), 17 slices, 3 × 3 × 7 mm3 resolution labeling duration = 1650 ms, initial postlabeling delay = 1525 ms, 30 control and label pairs] and a T1-weighted magnetization prepared rapid gradient echo (3D-magnetization prepared rapid gradient echo) for anatomical reference (TE/TR = 3.1/6.8 ms, 256 × 256 × 204 mm3 FOV, 1.1 × 1.1 × 1.2 mm3 in-plane resolution).

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Image processing

After acquisition, ASL postprocessing was performed to obtain quantitative CBF maps using the in-house developed ‘ExploreASL’ toolbox based on SPM12 (Statistical Parametric Mapping, Wellcome Trust Centre for Neuroimaging; London, UK). All control–label pairs were corrected for head motion, whereby pairs with severe head motion were excluded. The perfusion-weighted map was obtained by averaging the control minus label pairs, and converted to CBF values using the single compartment quantification model that has been previously described in detail [30]. A single M0 value was used for all participants, obtained in a previous study of healthy volunteers [31]. Considering the possible effect of HIV on hematocrit, and the influence that hematocrit has on the T1 relaxation rate of blood, CBF was quantified with patient-specific blood T1 values derived from the individual hematocrit measurements, instead of a commonly used reference value [32].

Gray matter probability maps were obtained from the anatomical reference scans by SPM12. In addition, the CBF map was registered to the gray matter probability map by rigid body registration (Fig. 1). Because of the relatively low signal of ASL in the white matter, white matter CBF was not analyzed. Gray matter volume was computed relative to the total intracranial volume to assess possible partial volume effects on CBF [33].

Fig. 1

Fig. 1

Gray matter probability maps were then used to create a population-based template in common space using diffeomorphic anatomical registration analysis using exponentiated Lie algebra, to which CBF maps were transformed using the same transformation parameters [34]. Based on this population-based gray matter probability template, a common gray matter mask was created (thresholded on 0.4).

To correct for CBF quantification differences between scanners (e.g. M0), mean gray matter CBF values were computed and used to linearly scale CBF maps per scanner, such that the mean gray matter CBF of the Ingenia patients was the same as the mean gray matter CBF of the Intera patients (after validating that both scanners had a comparable number of cases and controls). For voxel-based analysis (VBA), CBF maps were smoothed with a 7 mm full width at half maximum Gaussian kernel.

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Statistical analysis

Characteristics of HIV-infected participants and uninfected controls were compared using a chi-square test or Fisher's exact test (in cases in which n < 5 for at least one group) for categorical variables. Normally distributed continuous variables were compared using a Student's t test, and nonnormally distributed continuous variables were compared using a Mann–Whitney U test.

To reveal potential associations between CBF and HIV, the data were analyzed in two ways. First, using the mean gray matter CBF value as a measure for global brain perfusion and second using smoothed CBF-maps in a VBA to spatially delineate effects within the brain's gray matter. In addition, the association between mean gray matter CBF and cognitive function was assessed within the HIV-infected patient group.

The mean gray matter CBF value (hereafter referred to as CBF) was used as dependent variable in a regression model, with HIV-serostatus, age and scanner system as independent variables. In addition, identified determinants and confounders were incorporated into our statistical models as independent variables to adjust for their effects.

Two models were derived, with model 1 including both HIV-infected patients and HIV-uninfected controls and model 2 including HIV-infected patients only.

Biologically plausible determinants and confounders of CBF were selected from the cohort database, and their relations with CBF were probed by a stepwise regression model selection approach, with P less than 0.05 to enter and P more than 0.1 to remove.

In model 1, the following candidate variables were selected and examined:

  1. Intoxicants: reported cannabis use on daily to monthly basis (transformed to binary variable of absent or present), reported cocaine or ecstasy use on weekly to monthly basis (transformed to binary variables of absent or present), average units of alcohol consumed per week and tobacco consumption in pack-years;
  2. Comorbid conditions: history of CVD; including angina pectoris, myocardial infarction, or peripheral arterial disease (absent or present); hypertension (absent or present); diabetes mellitus type 2 (absent or present) and use of psychotropic medication (absent or present);
  3. Vascular disease risk factors: SBP and DBP, arterial stiffness based on pulse wave velocity [35,36], hemoglobin A1c, waist circumference, hip circumference, BMI, waist-to-hip ratio, HDL cholesterol and LDL cholesterol, triglycerides and lipoprotein(a); SCORE-low: a CVD score for low-risk regions in Europe [37], log-transformed to obtain a normal distribution for statistical analysis.
  4. Inflammation, immune activation and coagulation markers: high-sensitivity C-reactive protein, soluble CD14 and CD16 or D-dimer.

To identify additional HIV disease and antiretroviral therapy (ART)-related determinants of CBF, the following variables were additionally examined in model 2, that is within the HIV-infected population: known duration of HIV infection, being treatment-naive when starting cART (absent or present), duration of ART use, current and nadir CD4+ cell counts, the time spent with a CD4+ cell count below 500 cells/μl (lower boundary of normal range of CD4+ cell count), duration of undetectable plasma viral load, prior AIDS according to the Centers for Disease Control and Prevention classification (absent or present) and current/prior/duration of/use of individual antiretroviral agents.

To assess the association between CBF and cognitive function, regression analyses were performed within the HIV-infected group, with CBF as independent variable (adjusted for determinants and confounders as identified in the previous analyses) and cognitive function as dependent variable.

The VBA included variables that were found to have a significant effect on CBF in the preceding stepwise linear regression analysis. Gray matter volume was added as a nuisance variable to adjust for partial volume effects. Voxels were considered significant at family-wise error corrected P less than 0.05 using a P less than 0.001 (primary) cluster-forming threshold [38].

MNC was performed using R statistical software (R Developmental Core Team; Vienna, Austria), VBA in SPM12 and the remaining analyses were performed with SPSS (IBM SPSS Statistics for Windows, Version 20.0.; IBM Corp., Armonk, New York, USA).

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Results

Patient characteristics

A total of, 103 HIV-infected patients and 74 HIV-uninfected controls were enrolled between December 2011 and August 2013. Neuroimaging data were incomplete for three HIV-infected patients and five controls, such that data from 100 HIV-infected patients and 69 uninfected controls were used for analysis.

Table 1 provides an overview of the patient characteristics, including HIV disease and treatment factors. Although some differences were detected, HIV-infected patients and uninfected controls were generally comparable in terms of demographics, clinical characteristics and lifestyle-related factors (Table 1).

The HIV-infected patients were known to have been infected for a long period (median duration 13.4 years) and to have been treated with ART for most of this time (median duration 11.4 years). They had achieved substantial immune recovery on treatment, from a median nadir CD4+ cell count of 170 cells/μl to a current median CD4+ cell count of 628 cells/μl.

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Mean gray matter cerebral blood flow per group and its confounders and determinants

The median and interquartile ranges for CBF were 46.4 ml/100 g min (41.4–55.5) in HIV uninfected controls and 44.0 ml/100 g min (38.7–52.4) in HIV patients (P = 0.06; Table 1). When adjusting for potential confounders in model 1 (Table 2), HIV-seropositive status was significantly associated with lower CBF (P = 0.02). Ecstasy use and greater waist circumference were both associated with lower CBF (P = 0.04, P = 0.02, respectively). SCORE-low was significantly associated with CBF (P = 0.05), but was no longer an independent risk factor after adding waist circumference to the model (P = 0.17). A trend was found for higher age and lower CBF (P = 0.07). Normalizing measurements on both scanners for mean population gray matter CBF removed the effect of scanner system on CBF (model 1, Table 2).

Table 2

Table 2

When restricting the analysis to the HIV-infected patient group in model 2 (Table 2), greater waist circumference remained associated with lower CBF (P = 0.04). In addition, prior AIDS and higher triglyceride levels were associated with lower CBF (P = 0.03, P = 0.005, respectively). Higher D-dimer levels and longer time spent with a CD4+ cell count below 500 cells/μl were not significantly associated with lower CBF, although a trend was found for both associations. Increased age was not related to CBF, nor was the effect of scanner system.

No association was found between CBF and current or prior use of particular antiretroviral drugs. No associations were found between CBF and markers of innate immune activation. No other HIV-related or ART-related determinants of CBF were identified.

Replacing the variable waist circumference by the variable BMI resulted in comparable effects on CBF (effect of BMI on CBF in model 1 P = 0.02; model 2 P = 0.04).

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HIV, cerebral blood flow and cognition

Only 17% of HIV-infected patients were considered cognitively impaired, and none of them had HIV-associated dementia. HIV-seropositive status was significantly associated with poorer cognitive function (P = 0.04). No association was found between CBF (adjusted for age, scanner, ecstasy use and waist circumference) and cognitive function within the HIV-infected patient group (P = 0.64). Similarly, no association with GDS was found (P = 0.94).

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Voxel-based analysis

HIV-infected patients had lower gray matter volumes compared with the HIV-uninfected controls (P = 0.03, Table 1). The relation between lower gray matter volumes and CBF was addressed in the VBA. HIV-seropositive status remained associated with lower CBF after adjustment for age, ecstasy use, waist circumference and gray matter volume. The effect of HIV serostatus on CBF was found in different regions than the effects of gray matter-volume and age on CBF (Fig. 2).

Fig. 2

Fig. 2

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Discussion

Main results and general interpretation

We found that HIV-seropositive status was associated with decreased CBF, after adjusting for age, ecstasy use and waist circumference. The spatial analysis showed that the pattern of decreased CBF was widespread throughout the brain's gray matter. Within the HIV-infected group, decreased CBF was significantly associated with higher triglyceride levels and prior clinical AIDS. Although HIV-seropositive status was associated with both poorer cognitive function and decreased CBF, no association between CBF and cognition was found within the current cohort of HIV-infected patients on long-term successful treatment.

So far, few studies have examined CBF in HIV-infected patients within the cART era. One study reported increased CBF among treatment-naïve HIV-infected patients, which suggested increased metabolic activity due to HIV-associated inflammation [23]. Conversely, two studies on HIV-infected patients, of whom the majority were treated by cART, reported decreased CBF [20,24]. However, another study of HIV-infected patients on successful treatment reported a general association between increased age and decreased CBF within the frontal lobes of the brain but no group differences [22]. The latter may be explained by their smaller sample size; however, the general age effect that they observed was replicated in the current study.

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Determinants of cerebral blood flow

From the HIV-related parameters, we found that having experienced prior clinical AIDS was associated with decreased CBF. Lower CD4+ cell counts and higher plasma viral load have been associated with decreased CBF in earlier studies. This has been suggested to reflect injury associated with immune status or disease severity [39]. Within the current study, a trend was found between the time spent with reduced CD4+ cell counts and decreased CBF. The underlying pathological mechanism of such associations between measures of past immune deficiency and decreased CBF remains poorly understood, but may reflect vascular insult from direct toxic effects of HIV to the vessel walls and indirect proinflammatory responses of the vessel wall, reinforcing atherogenesis [5]. Raised concentrations of endothelial activation and inflammation markers in HIV-infected patients provide support for vessel wall inflammation in HIV [40]. Such vascular insult may have developed particularly in the period between HIV infection and initiation of effective ART, when viral toxicity and immune activation were at their peak (i.e. legacy effect).

Among HIV-infected patients, we found that higher triglyceride levels were associated with decreased CBF. This has previously been reported in studies concerning patients with metabolic syndrome and diabetes mellitus [41,42]. Elevated triglyceride levels are frequent in the context of HIV because of the increasingly recognized risk of metabolic complications in HIV, such as dyslipidemia, insulin resistance and hypertension [5,25,43]. This may suggest that lipid changes, including those that may be seen in conjunction with the use of certain cART regimens, may affect cerebral arteries and microcirculation and lead to hemodynamic changes [11,44].

We observed a significant association between a larger waist circumference and lower CBF, whereas there was no significant association with hip circumference (P = 0.19) and a borderline association with waist-to-hip ratio (P = 0.07). Visceral adiposity as a cardiovascular risk factor generally is known to be related to lower CBF [41]. These findings may suggest that, with respect to prior exposure to older antiretrovirals, exposure to some of the older HIV protease inhibitors known to be associated with lipohypertrophy, in this context may be more relevant than exposure to older thymidine analogue reverse transcriptase inhibitors that were associated with peripheral lipoatrophy. Both lipohypertrophy and lipoatrophy as part of the lipodystrophy syndrome have been associated with increased cardiovascular and metabolic risk. [45] Although lipoatrophy has become rare, weight gain and abdominal obesity continues to be frequently observed in patients first initiating treatment with contemporary ART regimens [46,47].

Ecstasy use was more prevalent among the controls than among the HIV-infected patients, although the occurrence was low in both groups (12 vs. 2%). The confounding result of ecstasy use that we have found on decreased CBF may result from ecstasy-induced subacute and prolonged vasoconstriction [48,49].

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Cerebral blood flow and HIV-associated cognitive impairment

The associations of CBF and cognitive impairment in HIV that have been reported thus far varied widely. One study examined patients with different degrees of HIV-related minor motor deficits and reported increased CBF among patients with early psychomotor slowing compared with patients with normal motor function or sustained pathological psychomotor slowing. This finding was suggested to reflect increased metabolic demand to compensate for cognitive decline [21]. In contrast, another study reported decreased CBF in patients with early stages of HIV-cognitive motor complex in comparison with controls [39]. Finally, one study specifically compared cognitively impaired with unimpaired HIV-infected patients and reported no differences in CBF [24]. Likewise, we were also unable to detect an association between CBF and cognitive function amongst our HIV-infected cohort. This lack of association could possibly be explained by the fact that only 17% of patients had cognitive deficits in this well defined cohort of aviremic HIV-infected patients on long-term cART. Also, given that the differences in CBF values were subtle, a possible relationship between CBF and cognitive impairment would be difficult to detect. Follow-up of our cohort of HIV-infected patients could provide more insight in the time course of hemodynamic alterations in relation to cognitive deterioration in the context of the aging HIV-infected population on long-term successful treatment.

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Strengths and limitations

Strengths of the current study include the comparison of our cohort of HIV-infected patients with a (demographic and lifestyle factor) comparable HIV-uninfected control group and the various assessments performed to characterize the participants. This allowed us to study the relationships of a broad range of clinical factors with CBF and to assess the effects of HIV more thoroughly. In addition, HIV-infected patients had significantly lower hematocrit values compared with HIV-uninfected controls. As the ASL signal is hematocrit-dependent, we have used the patient-specific hematocrit values in the CBF quantification rather than a commonly used reference value. Our study was limited by a scanner upgrade midway through the study, which was adjusted for methodologically by scaling CBF maps and statistically by incorporating it as a covariate in the analyses. Furthermore, the HIV-infected population examined within the current study consisted of exclusively male participants who were HIV-infected for a long period of time with sustained suppressed viremia on cART. Therefore, we are unable to make generalizations to other HIV-infected populations.

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Conclusion

When adjusting for age, ecstasy use and waist circumference, we observed decreased CBF in middle-aged HIV-infected men with sustained suppressed viremia on cART in comparison with HIV-uninfected, otherwise similar controls. Decreased CBF was associated with both vascular risk factors as well as with measures of past immune deficiency, but not with cognitive impairment. The results from the current study are suggestive for increased vascular disease in HIV, which may affect hemodynamic changes, but without overt cognitive consequences within the current cohort of patients on long-term successful treatment. Longitudinal follow-up studies are needed to provide further insight in the progression of such hemodynamic changes in relation to HIV-associated cognitive impairment.

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Acknowledgements

We thank all study participants without whom this research would not be possible. We thank all HIV physicians and HIV nurses at the Academic Medical Center for their efforts to include the HIV-infected participants into the AGEhIV Cohort Study, and the Municipal Health Service Amsterdam personnel for their efforts to include the HIV-uninfected participants into the AGEhIV Cohort Study. We thank Barbara Elsenga, Jane Berkel, Sandra Moll, Maja Totté and Marjolein Martens for running the AGEhIV study program and capturing our data with such care and passion. We thank Yolanda Ruijs-Tiggelman, Lia Veenenberg-Benschop, Tieme Woudstra, Sima Zaheri and Mariska Hillebregt at the HIV Monitoring Foundation for their contributions to data management. We thank Aafien Henderiks and Hans-Erik Nobel for their advice on logistics and organization at the Academic Medical Center. We thank Mathilde Kaper for carefully reviewing the manuscript.

Author contributions: T.S. contributed to the study design, was responsible for the data collection, data analysis and interpretation and conception of the manuscript. H.J.M.M.M. processed the MRI data, contributed to the interpretation of the data and reviewed and approved the final manuscript. M.W.A.C. contributed to the study design, supervised MRI data acquisition, processed the MRI data, contributed to data analysis and interpretation and reviewed and approved the final manuscript. F.W.N.M.W. contributed to the study design, supervised data analysis, reviewed and approved the final manuscript. J.S. and M.P. contributed to the study design, the data collection, data interpretation and reviewed and approved the final manuscript. G.J.G. contributed to the study design, supervised neuropsychological data acquisition, contributed to data interpretation and reviewed and approved the final manuscript. D.J.S. contributed to the study design, data interpretation and reviewed and approved the final manuscript. E.R. contributed to the interpretation of the data, critically revised the manuscript and approved the final manuscript. P.P. contributed to the study design, data interpretation and reviewed and approved the final manuscript. P.R. conceived the main cohort study and the substudy, contributed to both study designs, to data interpretation and reviewed and approved the final manuscript. C.B.M. conceived the substudy, acquired funding for the substudy, contributed to its design, contributed to data interpretation and reviewed and approved the final manuscript.

The current work was supported by the Nuts-OHRA Foundation (grant no. 1003-026), Amsterdam, The Netherlands, as well as by The Netherlands Organization for Health Research and Development (ZonMW) together with AIDS Fonds (grant nos. 300020007 and 2009063). Additional unrestricted scientific grants were received from Gilead Sciences, ViiV Healthcare, Janssen Pharmaceutica N.V., Bristol-Myers Squibb, Boehringer Ingelheim and Merck&Co.

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Conflicts of interest

F.W.N.M.W. has received travel grants from Gilead Sciences, ViiV Healthcare, Boehringer Ingelheim, Abbvie and Bristol-Myers Squibb. J.S. has received travel grants from Gilead Sciences, ViiV Healthcare and Boehringer Ingelheim. D.J.S. is funded by a National Institute of Health Research Professorship (NIHR-RP-011-048) and has received an investigator-led grant from Pfizer, unrelated to the current work. P.P. has been an ad-hoc advisor to or speaking at various events sponsored by ViiV Healthcare, Gilead Sciences, Abbvie and Bristol-Myers Squibb. P.R. through his institution has received independent scientific grant support from Gilead Sciences, Janssen Pharmaceuticals Inc., Merck&Co, Bristol-Myers Squibb and ViiV Healthcare. He has served on scientific advisory board for Gilead Sciences; he has served on a data safety monitoring committee for Janssen Pharmaceuticals Inc and has chaired a scientific symposium organized by ViiV Healthcare, for which his institution has received remuneration.

T.S., H.J.M.M.M., M.W.A.C., G.J.G., E.R., M.P. and C.M. have no conflicts of interest related to the current work. None of the funding bodies had a role in the design or conduct of the study, the analysis and interpretation of the results, or the decision to publish.

Part of these data were presented at the Sixth International Meeting on HIV Infection of the Central Nervous System in Matera, Italy, 8–10 October 2015.

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References

1. Wada N, Jacobson LP, Cohen M, French A, Phair J, Munoz A. Cause-specific life expectancies after 35 years of age for human immunodeficiency syndrome-infected and human immunodeficiency syndrome-negative individuals followed simultaneously in long-term cohort studies, 1984–2008. Am J Epidemiol 2013; 177:116–125.
2. Heaton RK, Clifford DB, Franklin DR Jr, Woods SP, Ake C, Vaida F, et al. HIV-associated neurocognitive disorders persist in the era of potent antiretroviral therapy: CHARTER Study. Neurology 2010; 75:2087–2096.
3. Robertson KR, Smurzynski M, Parsons TD, Wu K, Bosch RJ, Wu J, et al. The prevalence and incidence of neurocognitive impairment in the HAART era. AIDS 2007; 21:1915–1921.
4. Simioni S, Cavassini M, Annoni J-M, Rimbault Abraham A, Bourquin I, Schiffer V, et al. Cognitive dysfunction in HIV patients despite long-standing suppression of viremia. AIDS 2010; 24:1243–1250.
5. Benjamin La, Bryer A, Emsley HCa, Khoo S, Solomon T, Connor MD. HIV infection and stroke: current perspectives and future directions. Lancet Neurol 2012; 11:878–890.
6. González-Scarano F, Martín-García J. The neuropathogenesis of AIDS. Nat Rev Immunol 2005; 5:69–81.
7. Manji H, Jäger HR, Winston A. HIV, dementia and antiretroviral drugs: 30 years of an epidemic. J Neurol Neurosurg Psychiatry 2013; 84:1126–1137.
8. Soontornniyomkij V, Umlauf A, Chung SA, Cochran ML, Soontornniyomkij B, Gouaux B, et al. HIV protease inhibitor exposure predicts cerebral small vessel disease. AIDS 2014; 28:1297–1306.
9. Monsuez J-J, Goujon C, Wyplosz B, Couzigou C, Escaut L, Vittecoq D. Cerebrovascular diseases in HIV-infected patients. Curr HIV Res 2009; 7:475–480.
10. Grunfeld C. Dyslipidemia and its treatment in HIV infection. Top HIV Med 2011; 18:112–118.
11. Worm SW, Sabin C, Weber R, Reiss P, El-Sadr W, Dabis F, et al. Risk of myocardial infarction in patients with HIV infection exposed to specific individual antiretroviral drugs from the 3 major drug classes: the data collection on adverse events of anti-HIV drugs (D:A:D) study. J Infect Dis 2010; 201:318–330.
12. Deeks SG. HIV infection, inflammation, immunosenescence, and aging. Annu Rev Med 2011; 62:141–155.
13. Pantoni L. Cerebral small vessel disease: from pathogenesis and clinical characteristics to therapeutic challenges. Lancet Neurol 2010; 9:689–701.
14. Hanning U, Husstedt IW, Niederstadt T-U, Evers S, Heindel W, Kloska SP. Cerebral signal intensity abnormalities on T2-weighted MR images in HIV patients with highly active antiretroviral therapy: relationship with clinical parameters and interval changes. Acad Radiol 2011; 18:1144–1150.
15. Haddow LJ, Dudau C, Chandrashekar H, Cartledge JD, Hyare H, Miller RF, Jäger HR. Cross-sectional study of unexplained white matter lesions in HIV positive individuals undergoing brain magnetic resonance imaging. AIDS Patient Care STDS 2014; 28:341–349.
16. Steinbrink F, Evers S, Buerke B, Young P, Arendt G, Koutsilieri E, et al. Cognitive impairment in HIV infection is associated with MRI and CSF pattern of neurodegeneration. Eur J Neurol 2013; 20:420–428.
17. McMurtray A, Nakamoto B, Shikuma C, Valcour V. Small-vessel vascular disease in human immunodeficiency virus infection: the Hawaii aging with HIV cohort study. Cerebrovasc Dis 2007; 24:236–241.
18. Su T, Wit FW, Caan MW, Schouten J, Prins M, Geurtsen GJ, et al. White matter hyperintensities in relation to cognition in HIV-infected men with sustained suppressed viral load on combination antiretroviral therapy. AIDS 2016; 30:2329–2339.
19. van der Veen PH, Muller M, Vincken KL, Hendrikse J, Mali WPTM, van der Graaf Y, Geerlings MI. Longitudinal relationship between cerebral small-vessel disease and cerebral blood flow: the second manifestations of arterial disease-magnetic resonance study. Stroke 2015; 46:1233–1238.
20. Ances BM, Sisti D, Vaida F, Liang CL, Leontiev O, Perthen JE, et al. Resting cerebral blood flow: a potential biomarker of the effects of HIV in the brain. Neurology 2009; 73:702–708.
21. Wenserski F, Von Giesen H, Wittsack H, Aulich A, Arendt G. Human immunodeficiency virus 1 -associated minor motor disorders: perfusion-weighted MR Imaging and HMR spectroscopy. Radiology 2003; 228:185–192.
22. Towgood KJ, Pitkanen M, Kulasegaram R, Fradera A, Soni S, Sibtain N, et al. Regional cerebral blood flow and FDG uptake in asymptomatic HIV-1 men. Hum Brain Mapp 2013; 34:2484–2493.
23. Sen S, An H, Menezes P, Oakes J, Eron J, Lin W, et al. Increased cortical cerebral blood flow in asymptomatic human immunodeficiency virus-infected subjects. J Stroke Cerebrovasc Dis 2016; 25:1–5.
24. Ances BM, Roc AC, Wang J, Korczykowski M, Okawa J, Stern J, et al. Caudate blood flow and volume are reduced in HIV+ neurocognitively impaired patients. Neurology 2006; 66:862–866.
25. Schouten J, Wit FW, Stolte IG, Kootstra N, van der Valk M, Geerlings SG, et al. 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 2014; 59:1787–1797.
26. Su T, Schouten J, Geurtsen GJ, et al. Multivariate normative comparison, a novel method for more reliably detecting cognitive impairment in HIV infection. AIDS 2015; 29:547–557.
27. Huizenga HM, Smeding H, Grasman RPPP, Schmand B. Multivariate normative comparisons. Neuropsychologia 2007; 45:2534–2542.
28. Carey CL, Woods SP, Gonzalez R, Conover E, Marcotte TD, Grant I, Heaton RK. HNRC Group. Predictive validity of global deficit scores in detecting neuropsychological impairment in HIV infection. J Clin Exp Neuropsychol 2004; 26:307–319.
29. Schouten J, Su T, Wit FW, Kootstra NA, Caan MW, Geurtsen GJ, et al. Determinants of reduced cognitive performance in HIV-1-infected middle-aged men on combination antiretroviral therapy. AIDS 2016; 30:1027–1038.
30. Alsop DC, Detre JA, Golay X, Günther M, Hendrikse J, Hernandez-Garcia L, et al. Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: a consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia. Magn Reson Med 2015; 73:102–116.
31. Heijtel DFR, Mutsaerts HJMM, Bakker E, Schober P, Stevens MF, Petersen ET, et al. Accuracy and precision of pseudo-continuous arterial spin labeling perfusion during baseline and hypercapnia: a head-to-head comparison with 15O H2O positron emission tomography. NeuroImage 2014; 92:182–192.
32. Varela M, Hajnal JV, Petersen ET, Golay X, Merchant N, Larkman DJ. A method for rapid in vivo measurement of blood T1. NMR Biomed 2011; 24:80–88.
33. Binnewijzend MAA, Kuijer JPA, Benedictus MR, van der Flier WM, Wink AM, Wattjes MP, et al. Cerebral blood flow measured with 3D pseudocontinuous arterial spin-labeling MR imaging in Alzheimer disease and mild cognitive impairment: a marker for disease severity. Radiology 2013; 267:221–230.
34. Ashburner J. A fast diffeomorphic image registration algorithm. NeuroImage 2007; 38:95–113.
35. Horváth IG, Németh A, Lenkey Z, Alessandri N, Tufano F, Kis P, et al. Invasive validation of a new oscillometric device (Arteriograph) for measuring augmentation index, central blood pressure and aortic pulse wave velocity. J Hypertens 2010; 28:2068–2075.
36. Baulmann J, Schillings U, Rickert S, Uen S, Düsing R, Illyes M, et al. A new oscillometric method for assessment of arterial stiffness: comparison with tonometric and piezo-electronic methods. J Hypertens 2008; 26:523–528.
37. Conroy RM, Pyörälä K, Fitzgerald AP, Sans S, Menotti A, De Backer G, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: The SCORE project. Eur Heart J 2003; 24:987–1003.
38. Woo CW, Krishnan A, Wager TD. Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations. Neuroimage 2014; 91:412–419.
39. Chang L, Ernst T, Leonido-Yee M, Speck O. Perfusion MRI detects rCBF abnormalities in early stages of HIV-cognitive motor complex. Neurology 2000; 54:389–396.
40. Ross AC, Rizk N, O’Riordan MA, Dogra V, El-Bejjani D, Storer N, et al. Relationship between inflammatory markers, endothelial activation markers, and carotid intima-media thickness in HIV-infected patients receiving antiretroviral therapy. Clin Infect Dis 2009; 49:1119–1127.
41. Birdsill AC, Carlsson CM, Willette AA, Okonkwo OC, Johnson SC, Xu G, et al. Low cerebral blood flow is associated with lower memory function in metabolic syndrome. Obesity (Silver Spring) 2013; 21:1313–1320.
42. Rusinek H, Ha J, Yau PL, Storey P, Tirsi A, Tsui WH, et al. Cerebral perfusion in insulin resistance and type 2 diabetes. J Cereb Blood Flow Metab 2014; 35:95–102.
43. 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. Clin Infect Dis an Off Publ Infect Dis Soc Am 2011; 53:1120–1126.
44. de Gaetano Donati K, Rabagliati R, Tumbarello M, Tacconelli E, Amore C, Cauda R, Lacoviello L. Increased soluble markers of endothelial dysfunction in HIV-positive patients under highly active antiretroviral therapy. AIDS 2003; 17:765–768.
45. Hadigan C, Meigs JB, Corcoran C, Rietschel P, Piecuch S, Basgoz N, et al. Metabolic abnormalities and cardiovascular disease risk factors in adults with human immunodeficiency virus infection and lipodystrophy. Clin Infect Dis 2001; 32:130–139.
46. Hasse B, Iff M, Ledergerber B, Calmy A, Schmid P, Hauser C, et al. Obesity trends and body mass index changes after starting antiretroviral treatment: The Swiss HIV Cohort Study. Open Forum Infect Dis 2014; 1:ofu040.
47. Yuh B, Tate J, Butt AA, Crothers K, Freiberg M, Leaf D, et al. Weight change after antiretroviral therapy and mortality. Clin Infect Dis 2015; 60:1852–1859.
48. de Win MML, Jager G, Booij J, Reneman L, Schilt T, Lavini C, et al. Sustained effects of ecstasy on the human brain: a prospective neuroimaging study in novel users. Brain 2008; 131:2936–2945.
49. Reneman L, Majoie CB, Habraken JB, den Heeten GJ. Effects of ecstasy (MDMA) on the brain in abstinent users: initial observations with diffusion and perfusion MR imaging. Radiology 2001; 220:611–617.
Keywords:

aging; arterial spin labeling; cerebral blood flow; combination antiretroviral therapy; HIV-1-infection; HIV-associated cognitive impairment

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