The introduction of combination antiretroviral therapy (cART) has resulted in a dramatic decline in AIDS-associated mortality and morbidity, including AIDS Dementia Complex, which has largely disappeared from clinical practice [1–3]. Nonetheless, HIV-1-infected individuals on cART are increasingly reported to experience a broad range of non-AIDS related comorbidities, including cardiovascular, chronic liver and kidney disease, diabetes mellitus and cognitive impairment [4,5]. In the past few years, a high but varying prevalence of milder forms of cognitive impairment has been reported among HIV-1-infected individuals ranging from 15 to 69%, including those with systemically well controlled infection [6–11].
To classify this broadening clinical spectrum of HIV-1-associated neurocognitive disorders (HAND), a set of diagnostic criteria, commonly referred to as the Frascati criteria, has been developed (Table 1) . The Frascati criteria have a low threshold for detecting milder forms of HAND and may overestimate HAND prevalence . Thus, applying the Frascati criteria probably results in high false-positive rates. Consequently, the exact prevalence of HAND remains heavily debated . Gisslén et al. proposed modified criteria in an attempt to increase specificity (Table 1). A statistical method specifically designed to control false-positive rate while retaining sensitivity is multivariate normative comparison (MNC), a novel and potentially more accurate technique for evaluating cognitive impairment .
The purpose of this study was to determine the prevalence of HIV-1-associated cognitive impairment among HIV-1-infected men with suppressed viremia on cART compared with highly comparable uninfected male controls aged at least 45 years, and to assess whether MNC improves the detection of HAND as compared with Frascati and Gisslén criteria.
Materials and methods
Study design and participants
The AGEhIV Cohort Study is a prospective comparative cohort study investigating prevalence, incidence and risk factors of ageing-associated comorbidities and organ dysfunction among HIV-1-infected individuals and highly comparable HIV-uninfected controls. Inclusion criteria are age of at least 45 years and laboratory-confirmed presence or absence of HIV-1-infection.
HIV-1-infected participants were recruited at the HIV outpatient clinic of the Academic Medical Center in Amsterdam, The Netherlands, and HIV-uninfected controls from the ongoing Amsterdam Cohort Studies on HIV/AIDS and among persons attending the sexual health clinic of the Amsterdam Public Health Service (details concerning AGEhIV Cohort Study are provided in Supplementary no. 1, http://links.lww.com/QAD/A632 and have been described in a previous publication) .
All eligible participants from the main AGEhIV Cohort were consecutively invited to participate in a nested cognitive substudy, which began enrolment in December 2011. Additional eligibility criteria for the substudy were male sex (as the availability of Dutch-speaking women in the main AGEhIV Cohort was very limited), and for the HIV-1-infected group, sustained suppression of HIV viremia on antiretroviral treatment (plasma HIV-RNA <40 copies/ml) for at least 12 months; the presence of so-called viral ‘blips’ (transient low-level viremia) was not an exclusion criterion.
Exclusion criteria for the substudy were a history of severe neurological disorder [e.g. stroke, seizure disorders, multiple sclerosis, dementia (including previous or current diagnosis of HIV-associated dementia (HAD)], history of traumatic brain injury with loss of consciousness more than 30 min, current/past (HIV-1 associated) central nervous system infection or tumour, current severe psychiatric disorder (e.g. psychosis, major depression), current intravenous drug use, daily use of illicit drugs (with the exception of daily cannabis use), current excessive alcohol consumption (>48 units of alcohol/week), insufficient command of the Dutch language, and mental retardation. With respect to major depression as one of the exclusion criteria, depressive symptoms were assessed in the main AGEhIV Cohort Study by the nine-item Patient Health Questionnaire (PHQ-9). Participants with a PHQ-9 score of at least 15 (indicative of severe depressive symptoms and potentially of major depression) were excluded from participation in the substudy .
Standard protocol approval, registration and patient consent
The protocol of the AGEhIV Cohort Study (including the abovementioned substudy) was approved by the local ethics committee and has been registered at www.clinicaltrials.gov (identifier: NCT01466582). Written informed consent was obtained from all participants, both for the main study and substudy separately.
Neuropsychological assessment (NPA) was performed by trained neuropsychologists and covered six cognitive domains commonly affected by HAND, including fluency, attention, information processing speed, executive function, memory, and motor function (details concerning neuropsychological test battery are provided in Supplementary no. 2, http://links.lww.com/QAD/A632) . Depressive symptoms were assessed using the Beck Depression Inventory (BDI)  and subjective cognitive complaints with the Cognitive Failure Questionnaire (CFQ) . Everyday functioning was assessed using the Instrumental Activities of Daily Living (IADL) questionnaire  and premorbid intelligence was estimated by the Dutch Adult Reading Test (DART) .
Classification of neurocognitive impairment according to Frascati criteria, Gisslén criteria and multivariate normative comparison
First, Frascati criteria were applied to diagnose HAND . According to the Frascati criteria, participants are classified as having asymptomatic neurocognitive impairment (ANI) if at least one test per cognitive domain is at least 1 standard deviation (SD) below the normative mean for at least two cognitive domains, in the absence of interference with everyday functioning. Participants are classified as having mild neurocognitive disorder (MND) if they satisfy these same criteria, and if they report mild functional impairment. Participants are classified as having HAD if at least one test per cognitive domain is at least 2 SD below the normative mean for at least two cognitive domains, and if there is a marked functional impairment in daily life. Cognitive impairment should not be explained by opportunistic central nervous system disease, systemic illness, psychiatric illness, substance use disorders, or medications with central nervous system effects.
Second, modified Frascati criteria for HAND as proposed by Gisslén et al.  (from now on referred to as the Gisslén criteria) were applied. According to the Gisslén criteria, the definition of abnormal cognitive performance of ANI and MND should be modified by changing the cut-off, of preferably the mean domain performance, to below 1.5 SD to lower the false-positive rate. Also, concerning the criteria for HAD, the averaged domain performance should be at least 2 SD below the normative mean. All other criteria for HAND remained the same. See Table 1 for an overview of the Frascati and Gisslén criteria.
We used the CFQ-score as a surrogate for interference with everyday functioning to distinguish between ANI and MND, in which a cut-off score of 42 or higher (reflecting the 5% highest scores of the controls) was used to indicate a significant degree of subjective cognitive complaints. We used the IADL scale to assess functional impairment in daily life.
Finally, MNC was applied to diagnose cognitive impairment. MNC is a statistical method that may be seen as a multivariate version of Student's t-test for one sample. It can be used to statistically compare multiple cognitive scores of each single study participant against the distributions of the same scores of a control sample, taking the covariance between all test scores into account.
The MNC method is able to control family-wise error (the probability of making one or more false discoveries) by performing only one comparison in a multivariate manner. A complete cognitive profile is therefore compared in a single instance with the control sample, rather than comparing each test result separately to its norm. Thus, MNC was applied to compare the complete cognitive profile of each HIV-infected participant with the cognitive profile of the HIV-uninfected control group as a whole. The test statistic is Hotelling's T2. The false-positive rate, that is erroneously concluding that an individual deviates from the control sample while this is not the case, is limited by the level of significance (alpha). In the present study, alpha was set at 5% one-tailed. Consequently, the MNC has a specificity of at least 95% .
Normality of distribution and heterogeneity of variance were checked using the Kolmogorov–Smirnov test and Levene's test. Group comparisons were performed using Chi-square, Fisher's exact or Mann–Whitney U test as appropriate.
Neuropsychological test scores were converted to age and education corrected scores (z-scores) using normative standards, except for two tests for which demographically corrected norms were not available (PASAT and finger tapping). Therefore, we used the data of the controls to calculate age and education-corrected z-scores for these two tests. We performed a multivariate analysis of variance (MANOVA) for each cognitive domain to compare cognitive test scores of the HIV-infected and uninfected groups. Missing neuropsychological test scores (1.3%, due to colour blindness, severe hand injuries and hearing difficulty) were imputed by the average of participants with similar HIV-status, age and educational level.
MNC analyses were performed using R statistical software (http://purl.oclc.org/NET/RGRASMAN/MNC), while for remaining analyses, SPSS (version 20.0, IBM) was used. Inter-agreement percentages between the three classification methods were calculated.
One hundred and three HIV-1-infected and 74 HIV-uninfected participants were consecutively enrolled into the substudy between December 2011 and August 2013. Demographic and HIV-related characteristics are summarized in Table 2. Both groups were highly comparable, with a median age of 54 years in both groups, and the majority being MSM.
HIV-1-infected participants were known to be infected and treated with antiretroviral medication for a prolonged period of time, and 35% had previously been diagnosed with AIDS. The majority had experienced substantial immune recovery on treatment, with a median nadir CD4+ cell count of 170 cells/μl and current median CD4+ cell count of 625 cells/μl.
Factors related to cognition and behaviour are presented in Table 3. Both groups were comparable regarding educational level, number of depressive symptoms, and use of psychotropic medication. Ecstasy use was more prevalent among HIV-uninfected controls (12 vs. 2%, P = 0.008), whereas cannabis, cocaine, and alcohol use were comparable between the two groups.
Neuropsychological test results
HIV-1-infected individuals as a group performed worse compared with HIV-uninfected controls on the majority of cognitive tests. Statistically significant (one-tailed) small group differences were found for the cognitive domains: attention (P = 0.03) and executive function (P = 0.02). A trend was found for the cognitive domain information processing speed (P = 0.05). No significant group difference was found for the cognitive domains of fluency (P = 0.41), memory (P = 0.46) and motor function (P = 0.13). See Supplementary no. 3, http://links.lww.com/QAD/A632 for details.
Cognitive impairment by Frascati criteria, Gisslén criteria and multivariate normative comparison
Applying Frascati criteria, HAND was present in not only 49 of 103 HIV-1-infected men [48%; 95% confidence interval (95% CI) 38–58] but also in 27 of 74 HIV-uninfected men (36%; 95% CI 26–48; P = 0.09, one-tailed; Table 4). Applying Gisslén criteria, HAND was present in five of 103 HIV-1-infected men (5%; 95% CI 1–9) and one of 74 HIV-uninfected men (1%; 95% CI 1–3; P = 0.20, one-tailed).
Using MNC, cognitive impairment was detected in 17 HIV-1-infected men (17%; 95% CI 10–24). To verify the specificity of the MNC criterion, which was assumed to be at least 95%, we compared the scores of each individual HIV-uninfected control with the scores of the remaining control group (n = 73), using MNC. Four (5%; 95% CI 0–10) controls showed test results significantly deviating in a negative sense, supporting the assumption of 95% specificity.
Figure 1 depicts the profile of cognitive scores of the controls and HIV-1-infected participants with and without cognitive impairment as identified by MNC. Comparing HIV-1-infected participants with and without cognitive impairment according to MNC, those with cognitive impairment have lower median nadir CD4+ cell count (P = 0.001) and reported more often cannabis use (P = 0.001) (Table 2 and Table 3).
Agreement between the three classification methods
Frascati vs. Gisslén criteria showed an agreement of 60%, Frascati criteria vs. MNC 64%, and Gisslén criteria vs. MNC 90%.
HIV-1-infected individuals as a group performed worse compared with HIV-uninfected controls on all cognitive domains assessed. Differences were small but significant on the cognitive domains attention, executive function, and information processing speed.
Cognitive impairment as defined by Frascati criteria was highly prevalent in HIV-1-infected but nearly equally so in HIV-uninfected men. Although prevalence of HAND was markedly reduced when applying Gisslén criteria, it remained nearly as prevalent among HIV-uninfected controls. Applying MNC, a prevalence of 17% of HAND was found in HIV-1-infected men with suppressed viremia on cART.
HIV-1-infected individuals with cognitive impairment, as classified using MNC, had lower nadir CD4+ cell counts and reported more often cannabis use.
Earlier studies investigating HAND as defined by Frascati criteria reported prevalences ranging from 25 to 74% [6,7,9–11,21–23]. This broad range was narrowed to 19–38% when Frascati criteria were applied more conservatively, for example using averaged domain scores instead of individual tests scores [7,11,22–24]. Two studies reported higher HAND prevalences of 59 and 74% [10,25] than the prevalence we found (48%). These studies included larger proportions of hepatitis C virus (HCV) coinfected participants than our HIV-infected group and HCV coinfection is assumed to worsen cognitive status . In addition, these studies included participants with prior neurological diseases known to affect cognition, whereas such comorbidity was an exclusion criterion in our study. One study reported lower prevalence of HAND, that is 25%, which might be explained by the absence of comorbidity in their HIV-infected sample . Two studies reported comparable HAND prevalence to what we observed, that is 37 and 49% [27,28], even though their participants were slightly younger than in our study, and one of these studies had a larger proportion of participants with HCV coinfection. Most of the abovementioned studies did not include an HIV-uninfected control group, except for four studies [21,24,27,29]. Only two studies reported prevalence of cognitive impairment among controls as classified by Frascati, both of 13%, which is lower than our finding of 37%. This difference may be explained by the fact that our HIV-infected and HIV-uninfected groups had similar lifestyle and risk factor profiles.
The Frascati criteria are heavily debated, as they result in high false-positive rates and are likely to overestimate HAND prevalence. We found HAND by Frascati criteria to be highly prevalent in HIV-infected participants, but nearly equally so in uninfected controls, indeed confirming low specificity of this method. The Frascati criteria suffer two major shortcomings. Firstly, a cut-off of 1 SD below the normative mean is used for the diagnosis of HAND-subcategories ANI and MND. Given normally distributed test scores, 16% of the normal population will perform 1 SD below the mean on a given test.
The second shortcoming of the Frascati criteria is that an abnormal score on a single test is sufficient to classify the performance of an individual in a particular cognitive domain as abnormal. However, multiple tests are typically performed during an NPA. This increases the chance of erroneously drawing the conclusion that a result is abnormal. This is the so-called family-wise error .
Another issue concerning the Frascati criteria is that they do not dictate how to handle multiple testing within one cognitive domain. As a result, various interpretations of the Frascati criteria have been used by different studies.
To overcome the first shortcoming of the Frascati criteria, Gisslén et al. proposed to modify the definition of ANI and MND by changing the cut-off for abnormality to 1.5 SD below the population mean [13,31]. In addition, two alternative solutions have been proposed to overcome the second shortcoming of the Frascati criteria: first, to use averaged cognitive domain scores instead of individual cognitive tests and second, to limit assessment to three to five cognitive domains, in order to limit the number of comparisons . Although the number of comparisons is reduced by these solutions, multiple comparisons are still performed. MNC addresses the shortcomings of the Frascati criteria more adequately by performing only one comparison per individual, and comparing the complete cognitive profile of one individual with the complete cognitive profile of the control group in one instance. It reduces the false-positive rate, while enabling comprehensive neuropsychological examination. Furthermore, MNC is able to detect deviations in patterns from the norm . This is highly relevant as HAND is mild, with subtle abnormalities across a broad range of cognitive domains . In addition, there is a large variability of cognitive performance in the normal population, and therefore, small deviations from the norm cannot be easily detected . Moreover, variability in performance of a person across multiple tasks may be a more sensitive predictor for impaired daily functioning than his average cognitive performance . MNC has proven to be successful in detection of subtle cognitive impairment in Parkinson's disease [14,35], and it was applied to detect deviant profiles of white matter lesion load across brain regions .
When we applied the Gisslén criteria, the false-positive rate was greatly reduced but so was HAND prevalence, indicating reduced sensitivity. Applying MNC with its inherently low false-positive rate, we found cognitive impairment in 17% of HIV-infected participants. Specificity was indeed at least 95% in the present study.
Comparing HIV-1-infected participants with and without cognitive impairment as diagnosed by MNC, cannabis use was more common and the nadir CD4+ cell count was lower among HIV-1-infected individuals with cognitive impairment. Nadir CD4+ cell count has been associated with HAND [9,11,23,37], which suggests that cognitive impairment might be a residual effect of prior periods of severe immune suppression. Cannabis use is common among HIV-infected individuals for therapeutic and recreational purposes. More cognitive deficits among HIV-infected individuals with chronic cannabis use than nonusers have been reported before . In addition, effects of chronic cannabis use on brain metabolites of neuronal dysfunction and glial activation in HIV-infected individuals have been found .
There are some limitations of the current study. First, we used the CFQ to measure subjective cognitive complaints. It can be debated whether this tool is appropriate to estimate functional impairment. However, similar surrogates of functional decline were used in earlier studies [6,10]. These studies measured subjective cognitive complaints in a more qualitative way, whereas we used a standardized, multi-item questionnaire. Second, we may have missed some cases of HAD as we assessed activities of daily living by a self-report only, without collecting additional information from an informant. It is conceivable that ANI or MND cases who scored 2 SD or worse below the mean on cognitive tests may have had a lack of insight in their functional impairment. Third, ecstasy use was more often reported by the HIV-uninfected participants, which may affect cognitive performances, particularly verbal memory . Except for this difference, the HIV-uninfected control group is very similar to our group of HIV-1-infected individuals. Despite this higher ecstasy use among controls, the classification accuracy in our study is probably greater than when we had compared our patients with the general population adjusting only for effects of age and education. Fourth, our groups consist exclusively of male participants. Additional studies are needed to determine whether these results can equally be applied to women. Finally, this cohort is relatively small, and we were therefore unable to make broad generalizations.
The MNC method, unique by its multivariate nature facilitating profile analysis, is a powerful tool with a high specificity to detect HIV-associated cognitive impairment, which is characterized by multiple subtle deficits across a broad range of cognitive domains.
We thank Renée Baelde, Marleen Raterink and Michelle Klein-Twennaar for their assistance in neuropsychological testing. We thank Joost Zandvliet for his assistance in statistical computing in R.
We thank Katherine Kooij and Rosan van Zoest for their excellent coorganization of the cognitive substudy, as well as the main cohort study.
We thank psychiatrists Ieke Visser and Eric Ruhé for their useful advice and support concerning capturing and interpreting depressive symptoms.
We thank Tessa van der Knijff for monitoring, adjusting and improving our neuropsychological dataset.
We thank our colleagues at the Department of Experimental Immunology at the Academic Medical Center for the excellent collaboration both logistically and scientifically.
We thank Barbara Elsenga, Aafien Henderiks, Jane Berkel, Sandra Moll and Marjolein Martens for running the AGEhIV study programme 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 organisation at the Academic Medical Center.
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.
We thank all Municipal Health Service Amsterdam personnel for their efforts to include the HIV-uninfected participants into the AGEhIV Cohort Study.
We thank all study participants without whom this research would not be possible.
AGEhIV Cohort Study Group members are as follows:
Scientific oversight and coordination: P. Reiss (principal investigator), F.W.N.M. Wit, M. van der Valk, J. Schouten, K.W. Kooij, R.A. van Zoest, B.C. Elsenga [Academic Medical Center (AMC), Department of Global Health and Amsterdam Institute for Global Health and Development (AIGHD)].
M. Prins (coprincipal investigator), I.G. Stolte, M. Martens, S. Moll, J. Berkel, L. Möller, G.R. Visser, C. Welling (Public Health Service Amsterdam, Infectious Diseases Research Cluster).
Data management: S. Zaheri, M.M.J. Hillebregt, L.A.J. Gras, Y.M.C. Ruijs, D.P. Benschop, P. Reiss (HIV Monitoring Foundation).
Central laboratory support: N.A. Kootstra, A.M. Harskamp-Holwerda, I. Maurer, M.M. Mangas Ruiz, A.F. Girigorie, E. van Leeuwen (AMC, Laboratory for Viral Immune Pathogenesis and Department of Experimental Immunology).
Project management and administrative support: F.R. Janssen, M. Heidenrijk, J.H.N. Schrijver, W. Zikkenheiner (AIGHD), M. Wezel, C.S.M. Jansen-Kok (AMC).
Participating HIV physicians and nurses: S.E. Geerlings, M.H. Godfried, A. Goorhuis, J.T.M. van der Meer, F.J.B. Nellen, T. van der Poll, J.M. Prins, P. Reiss, M. van der Valk, W.J. Wiersinga, F.W.N.M. Wit; J. van Eden, A. Henderiks, A.M.H. van Hes, M. Mutschelknauss, H.E. Nobel, F.J.J. Pijnappel, A.M. Westerman (AMC, Division of Infectious Diseases).
Other collaborators: J. de Jong, P.G. Postema (AMC, Department of Cardiology); P.H.L.T. Bisschop, M.J.M. Serlie (AMC, Division of Endocrinology and Metabolism); P. Lips (VU University Medical Center Amsterdam); E. Dekker (AMC, Department of Gastroenterology); S.E.J.A. de Rooij (AMC, Division of Geriatric Medicine); J.M.R. Willemsen, L. Vogt (AMC, Division of Nephrology); J. Schouten, P. Portegies, B.A. Schmand, G.J. Geurtsen, J.A. ter Stege, M. Klein Twennaar (AMC, Department of Neurology); B.L.F. van Eck-Smit, M. de Jong (AMC, Department of Nuclear medicine); D.J. Richel (retired) (AMC, Division of Clinical Oncology); F.D. Verbraak, N. Demirkaya (AMC, Department of Ophthalmology); I. Visser, H.G. Ruhé (AMC, Department of Psychiatry); P.T. Nieuwkerk (AMC, Department of Medical Psychology); R.P. van Steenwijk, E. Dijkers (AMC, Department of Pulmonary medicine); C.B.L.M. Majoie, M.W.A. Caan, T. Su (AMC, Department of Radiology); H.W. van Lunsen, M.A.F. Nievaard (AMC, Department of Gynaecology); B.J.H. van den Born, E.S.G. Stroes, (AMC, Division of Vascular Medicine); W.M.C. Mulder (HIV Vereniging Nederland).
This work was supported by the Nuts-OHRA Foundation (grant no. 1003-026), Amsterdam, The Netherlands, as well as by The Netherlands Organisation for Health Research and Development (ZonMW) together with AIDS Fonds (grant nos. 300020007 and 2009063, respectively). Additional unrestricted scientific grants were received from Gilead Sciences, ViiV Healthcare, Janssen Pharmaceutica N.V., Bristol-Myers Squibb, Boehringer Ingelheim and Merck&Co.
None of these 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.
T.S. contributed to data collection, data analysis and interpretation, and writing of the manuscript.
J.S. contributed to data collection, data analysis and interpretation, and was responsible for producing and submitting the final manuscript.
G.G. contributed to data interpretation, and writing of the manuscript.
F.W. contributed to the study design, data analysis and interpretation, and writing of the manuscript.
I.S. contributed to the study design, data collection, data interpretation and writing of the manuscript.
M.P. contributed to the study design, data interpretation and writing of the manuscript.
P.P. contributed to study design, data interpretation and writing of the manuscript.
M.C. contributed to data interpretation, and writing of the manuscript.
P.R. conceived the main cohort study and the substudy, contributed to both study designs, to data interpretation and writing of the manuscript.
C.M. conceived the substudy, contributed to its design, to data interpretation and writing of the manuscript.
B.S. contributed to study design, supervised data analysis and interpretation, and supervised and contributed to writing of all drafts of the manuscript.
Conflicts of interest
T.S. has received travel grants from Boehringer Ingelheim.
J.S. has received travel grants from Gilead Sciences, ViiV Healthcare and Boehringer Ingelheim.
G.G., I.S., M.P., C.M., B.S. have no conflicts of interest.
F.W. has received travel grants from Gilead Sciences, ViiV Healthcare, Boehringer Ingelheim, Abbvie and Bristol-Myers Squibb.
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.
M.C. has received travel grants from Boehringer Ingelheim.
P.R. through his institution has received independent scientific grant support from Gilead Sciences, Janssen Pharmaceuticals Inc., Merck&Co, Bristol-Myers Squibb, Boehringer Ingelheim and ViiV Healthcare, and travel support through his institution from Gilead Sciences and Janssen Pharmaceuticals Inc. In addition, he has served on a scientific advisory board for Gilead Sciences and serves on a data safety monitoring committee for Janssen Pharmaceutica N.V., for which his institution has received renumeration.
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