Introduction
With the introduction of combination antiretroviral therapy (cART), AIDS-associated mortality and morbidity have markedly diminished and HIV encephalopathy, previously known as AIDS dementia complex, has largely disappeared [1–3]. In the past few years, however, a high prevalence (15–69%) of milder forms of cognitive impairment has been reported among HIV-infected individuals, including those with systemically well controlled HIV infection [4–9].
To classify this broad clinical spectrum of HIV-associated neurocognitive disorders, a set of diagnostic criteria, commonly referred to as Frascati criteria, was developed [10]. These criteria, however, appear oversensitive, resulting in not only high prevalence estimates but also high false-positive rates. We recently reported multivariate normative comparison (MNC), a technique which controls the false-positive rate while retaining sensitivity, to be a more accurate method of detecting cognitive impairment in the HIV-infected population [11].
In this previous report, we found cognitive impairment by MNC to be present in 17% of 103 HIV-infected men and in 5% of 74 HIV-uninfected controls participating in the AGEhIV Cohort Study (P = 0.02, one-tailed). Applying Frascati criteria to the same study population, cognitive impairment was highly prevalent in HIV-infected participants (48%), but nearly equally so in HIV-uninfected controls (36%, P = 0.09, one tailed), indicating a high-false positive rate [11].
In the pre-cART era, HIV-specific factors such as HIV viral load and CD4+ cell count were most strongly associated with cognitive impairment [12]. In cART-treated (and aging) individuals, however, the relative contribution of other risk factors towards cognitive impairment, including cardiovascular, metabolic, and other comorbid conditions, is likely to gain relative importance besides HIV/ART-specific factors such as persistent immune activation and inflammation [13]. The relative contribution of each of such factors to the pathogenesis of cognitive impairment remains to be further elucidated.
The purpose of this current study was to explore possible determinants for decreased cognitive performance as determined by MNC in the same abovementioned AGEhIV Cohort Study population. Within this study, which investigates age-associated comorbidity among middle-aged individuals with and without HIV-1 infection, a nested substudy was established focusing on cognitive functioning. We performed cross-sectional analyses on these 103 HIV-1-infected and 74 HIV-uninfected substudy participants, exploring a broad range of possible determinants for decreased cognitive performance including HIV/ART-related factors, inflammatory markers, use of illicit drugs and/or alcohol, psychiatric conditions, and metabolic and cardiovascular risk factors.
Methods
Study design and participants
The AGEhIV Cohort Study is a prospective 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, aged at least 45, in Amsterdam, The Netherlands, the details of which have been previously described [14].
At baseline, and every 2 years thereafter, participants undergo extensive screening for age-associated comorbidity and organ dysfunction.
All eligible participants from the main AGEhIV Cohort were consecutively invited to participate in a nested cognitive substudy, which began enrolment in December 2011 [11]. Additional eligibility criteria for the substudy were male sex (as the availability of native Dutch-speaking women in the main AGEhIV Cohort was limited), and for the HIV-1-infected group, sustained suppression of HIV-1 viraemia on antiretroviral treatment (plasma HIV-1 RNA <40 copies/ml) for at least 12 months; the presence of so-called viral ‘blips’ (transient low-level viraemia between 40 and 200 copies/ml) 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)], history of traumatic brain injury with loss of consciousness for more than 30 min, current/past (HIV-associated) central nervous system infection or tumour, current severe psychiatric disorder (e.g. psychosis and 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.
Individuals with a previous or current diagnosis of HIV-associated dementia were excluded from participation as they most likely already underwent interventions (e.g. adaptation of their antiretroviral treatment), biasing the results of our study.
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. Participants with a nine-item Patient Health Questionnaire score of at least 15 (indicative of severe depressive symptoms and high risk of major depression) were excluded from the participation in the substudy [15].
The inclusion/exclusion criteria with regard to illicit drug use (allowing weekly to monthly use of cocaine or ecstasy, as well as daily cannabis use) were implemented to minimize selection bias and were based on illicit drug use prevalence data previously obtained from the main AGEhIV Cohort. These showed daily cannabis use and weekly to monthly cocaine or ecstasy use to be fairly common among both HIV-1-infected participants attending the HIV outpatient department and HIV-uninfected controls [14].
Standard protocol approvals, registrations, and patient consents
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, separately for the main cohort study and nested substudy.
Neuropsychological assessment
As part of the substudy, neuropsychological assessment was performed by trained neuropsychologists and covered six cognitive domains commonly affected by HIV-associated cognitive impairment, including fluency, attention, information processing speed, executive function, memory, and motor function (details are provided in a previous publication) [11]. Depressive symptoms were assessed using the Beck Depression Inventory [16], and subjective cognitive complaints with the Cognitive Failures Questionnaire [17]. Everyday functioning was assessed using the Instrumental Activities of Daily Living [18] questionnaire and premorbid intelligence was estimated by the Dutch Adult Reading Test [19]. Use of psychotropic medication was assessed and included antidepressants, benzodiazepines, and methylphenidate.
Definitions
All definitions of investigated variables are provided as footnotes in Tables 1 and 2.
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Table 1: Baseline demographic and HIV-1-related characteristics.
Table 2: Baseline characteristics related to cognition, behaviour, comorbidity, and inflammation.
Cognitive impairment diagnosis by multivariate normative comparison
MNC is a statistical method that may be seen as a multivariate version of Student's t test for one sample [11,20]. MNC is able to control the family-wise error (the probability of falsely diagnosing individuals as cognitively abnormal) by performing a single multivariate comparison of the complete cognitive profile of a particular patient to the distribution of all the cognitive profiles of the control sample, rather than comparing each test result separately to the reference population. MNC thus compares the complete cognitive profile of each HIV-1-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 (α). In the present study, α was set at 5% one tailed, resulting in a specificity of at least 95%, as confirmed in our previous publication [11]. In that previous report, the false-positive rate for cognitive impairment was shown to be much higher when applying Frascati criteria, and was greatly reduced by applying MNC, indicating MNC to be a very powerful and more accurate tool for detecting cognitive impairment.
Multivariate normative comparison: cognitive impairment as a dichotomous measure and cognitive performance as a continuous measure
Applying MNC as described in the previous subsection provides a dichotomous result (cognitive impairment vs. no cognitive impairment). As the number of cognitively impaired participants in our cohort, as diagnosed by MNC, was relatively small, statistical power to investigate determinants was limited.
MNC, however, also provides a continuous measure: the Hotelling's T2 statistic. The Hotelling's T2 statistic reflects the degree of cognitive deviation of each HIV-1-infected participant compared with the HIV-uninfected control group as a whole.
The direction of the deviation (better or worse cognitive performance compared with the control population) was determined using the sum of all z-scores of the participant (being a positive or negative score). Hotelling's T2 statistics were then transformed to a normal distribution by subtracting the lowest absolute Hotelling's T2 statistic from all absolute Hotelling's T2 statistics. This way the bimodal curve of the Hotelling's T2 statistic was transformed to a curve with a single peak, approaching a normal distribution (as confirmed by skewness and kurtosis tests).
This continuous measure enabled us to perform more robust statistical analyses (linear instead of logistic regression) and increased statistical power. We therefore used this variable as the main outcome measure in the regression analyses.
Statistical analysis
Group comparisons were performed using the nonparametric test for trend, χ2, Fisher's exact, or Wilcoxon rank-sum test as appropriate.
Determinants for decreased cognitive performance were analyzed by linear regression using the Hotelling's T2 statistic from the MNC analysis as a continuous variable as outcome measure. As a sensitivity analysis, determinants for cognitive impairment as dichotomized by MNC, were analyzed by logistic regression. All regression analyses were restricted to the HIV-1-infected study group.
Plausible determinants of cognitive performance were analyzed using a forward stepwise model selection with P less than 0.05 as entry and P more than 0.1 as exit criterion, exploring the following categories of variables:
- demographic factors (age, premorbid IQ, educational level, Dutch as native language)
- coinfections (chronic hepatitis B/C virus coinfections)
- factors related to psychiatric comorbidity (depressive symptoms, psychotropic medication use)
- use of illicit drugs (cannabis/cocaine/ecstasy) and/or alcohol
- cardiovascular and metabolic factors (hypertension, smoking, diabetes mellitus type 2, BMI, waist-to-hip ratio, cardiovascular disease, levels of total/HDL/LDL cholesterol, triglycerides, and lipoprotein(a), physical activity, positive family history for myocardial infarction/hypertension/hypercholesterolaemia, renal function)
- markers of inflammation, monocyte activation, and coagulation [high-sensitivity C-reactive protein (hsCRP), soluble CD14 (sCD14), soluble CD163 (sCD163), D-dimer]
- HIV/ART-related factors (time since HIV-1 diagnosis, HIV-1 diagnosis prior to 1996, having been treated with mono or dual nucleoside-analogue reverse transcriptase inhibitors prior to starting cART, duration of ART use, duration/degree of immune deficiency, prior AIDS diagnosis, central nervous system penetration effectiveness score of the currently used cART regimen, current/prior/duration of/use of individual (classes) of antiretroviral agents
MNC analyses were performed using R statistical software (http://purl.oclc.org/NET/RGRASMAN/MNC); for remaining analyses, STATA (version 10.1; StataCorp, College Station, Texas, USA) was used.
Results
Participants’ characteristics
One hundred and three HIV-1-infected and 74 HIV-uninfected men were consecutively enrolled into the substudy between December 2011 and August 2013. Demographic and HIV-related characteristics are shown in Table 1. Both the groups were highly comparable, with a median age of 54 in both the groups, the majority of whom were MSM.
HIV-1-infected men 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 cART, with a median nadir CD4+ cell count of 170 cells/μl, current median CD4+ cell count of 625 cells/μl, and undetectable plasma viral load for a median 8 years.
Factors related to cognition, behaviour, comorbidity, and inflammation are presented in Table 2. Both groups were comparable regarding native language, educational level, premorbid intelligence, depressive symptoms, and use of psychotropic medication. Smoking was more prevalent among HIV positives (30 vs. 19% currently smoking, P = 0.048) and ecstasy use was more prevalent among HIV-uninfected controls (13 vs. 2%, P = 0.008), whereas cannabis, cocaine, and alcohol use were comparable between the two groups. Among HIV-positives, BMI was significantly lower [24.1 (interquartile range, IQR, 22.2–26.0) vs. 25.4 (IQR 23.7–27.5) kg/m2, P = 0.003] and waist-to-hip ratio significantly higher [0.96 (IQR 0.92–1.01) vs. 0.93 (IQR 0.89–0.99), P = 0.02]. Total, HDL, and LDL cholesterol, lipoprotein(a), and triglyceride levels were comparable between the two groups, as was use of lipid-lowering medication, physical activity, family history for metabolic/cardiovascular disease, history of cardiovascular disease, diabetes mellitus type 2, hypertension, and estimated glomerular filtration rate. Increased urinary albumin-to-creatinine ratio (≥3 mg/mmol) was significantly more prevalent among HIV positives (19.2 vs. 5.8%, P = 0.01). Levels of hsCRP and sCD14 were significantly higher among HIV positives [1.5 (IQR 0.7–3.3) vs. 1.1 (IQR 0.6–2.1) mg/l, P = 0.02 and 1548 (IQR 1318–2025) vs. 1207 (IQR 995–1558) ng/ml, P < 0.001, respectively]. hsCRP levels above 10 mg/l were also significantly more prevalent among HIV positives (10 vs. 0%, P = 0.005). D-dimer and sCD163 levels were comparable between the two study groups.
Cognitive impairment as diagnosed by multivariate normative comparison
As previously reported, using MNC, cognitive impairment was detected in 17 (17%) HIV-1-infected men. Transformed Hotelling's T2 statistics of the HIV-1-infected men ranged between −2.39 and 1.90, with a median of −0.15 (IQR −0.87 to +0.48).
Determinants of decreased cognitive performance by multivariate normative comparison in HIV-1-infected cohort participants
Linear regression analysis showed cannabis use, history of prior cardiovascular disease (borderline), impaired renal function (borderline), diabetes mellitus type 2, having an above-normal waist-to-hip ratio (borderline), presence of depressive symptoms (borderline), and lower nadir CD4+ cell count to be independently associated with poorer cognitive performance (Table 3, Model 1).
Table 3: Determinants for cognitive performance/impairment as determined by multivariate normative comparison.
Determinants of cognitive impairment as dichotomized by multivariate normative comparison in HIV-1-infected cohort participants (sensitivity analysis)
Logistic regression analysis showed cannabis use, history of prior cardiovascular disease, impaired renal function, and diabetes mellitus type 2 (borderline) to be independently associated with cognitive impairment (Table 3, Model 2).
Discussion
Key results
Determinants for decreased cognitive performance by MNC, when used as a continuous variable, included cannabis use, history of prior cardiovascular disease, impaired renal function, diabetes mellitus type 2, having an above-normal waist-to-hip ratio, presence of depressive symptoms, and lower nadir CD4+ cell count.
The first four determinants were also observed in a sensitivity analysis for which cognitive impairment was dichotomized as being present or absent by MNC. The latter three variables were not significant determinants in this analysis.
Interpretation, limitations, and conclusion
To appreciate these findings, some aspects of the current report need to be addressed further. Strong features of the AGEhIV Cohort Study and its nested substudy are the large similarity between the HIV-1-infected and the HIV-uninfected study groups, as well as the high level of detail by which all participants have been characterized. In addition, extensive clinical and biochemical data were obtained allowing for detailed assessment of relationships and adjustment for confounding.
Our results being those of cross-sectional analyses, we are merely able to demonstrate associations rather than causality. Although the HIV-1-infected and HIV-uninfected study groups were largely comparable, differences in some demographic and lifestyle-related factors were present, which was addressed by exploring the effect of each factor towards cognitive (dys)function, and incorporating adjustment for those factors with a significant effect. Nonetheless, differences in remaining unmeasured confounders potentially influencing our results cannot be excluded.
In addition, some unique characteristics of this cohort (participants being mostly white middle-aged MSM with sustained viral suppression, with a low prevalence of chronic hepatitis B and C) may limit generalization of the results to other populations. Additional studies are needed to determine whether our findings apply equally to other populations with different characteristics.
When analyzing determinants of cognitive impairment/performance by MNC, we found cannabis use to be strongly associated with cognitive dysfunction. Both in the general population and among HIV positives, cannabis use has been associated with decreased cognitive function [21,22]. In the context of HIV infection, cannabis use is common, not only for recreational but also for medicinal use (treating neuropathic pain, anorexia, nausea, or mood disturbances) [23,24]. In addition to direct effects of cannabis on cognition, the observed association could also be partly explained by some of the abovementioned conditions for which medicinal use of cannabis is indicated, which themselves may be associated with effects on cognition. The underlying reason for cannabis use (medicinal vs. recreational) unfortunately was not captured as part of data collection, and we were therefore unable to explore this hypothesis further.
We also found multiple metabolic/cardiovascular factors to be associated with cognitive impairment as well as decreased cognitive performance.
Both in the general population and among HIV positives, hypercholesterolaemia, diabetes mellitus type 2, and central obesity have been associated with decreased cognitive function [25–35].
We also found (prior) cardiovascular disease (i.e. angina pectoris, myocardial infarction, or peripheral arterial disease), to be associated with cognitive impairment/performance. In both the general and HIV-infected population, prior cardiovascular disease and subclinical atherosclerotic disease have been associated with cognitive decline [28,31,36–38].
In addition, we found albuminuria to be associated with cognitive dysfunction, which is in line with other studies, both in the general and the HIV-infected population [38–40].
Interpreting these results, cardiovascular/metabolic factors may substantially contribute to poorer cognitive performance among HIV-infected individuals. Cerebral damage resulting from (micro)vascular disease may therefore importantly contribute towards HIV-associated cognitive impairment. Several neuroimaging studies among HIV-infected individuals have also demonstrated cardiovascular/metabolic factors to be associated with cerebral damage, thereby supporting this hypothesis [41–43].
Evidence of renal impairment and past cardiovascular disease (each of which are associated with cognitive dysfunction in our analyses) are likely manifestations of (micro)vascular organ damage in many cases, and may (partly) share pathophysiological mechanisms with cerebral damage.
Presence of depressive symptoms was identified as an additional risk factor for decreased cognitive performance (but not for cognitive impairment). In the general population, depression has been associated with cognitive deficits [44]. Among HIV-infected individuals, depressive symptoms have also been associated with decreased cognitive function [45], although one study did not report an association between cognitive function and depressive symptoms [46].
We also found severity of prior immune deficiency, as reflected in a lower nadir CD4+ cell count, to be associated with decreased cognitive performance, which is also consistent with the earlier findings [12,47–49].
Although HIV infection is known to cause immune deficiency by depleting CD4+ cells, it is also associated with activation of the immune system and inflammation. This is partly driven by depletion of CD4+ cells within the intestinal mucosa resulting in increased permeability and translocation of microbial products across the mucosa. This results in stimulation of both the innate and adaptive immune systems that persists, albeit at a reduced level, among cART-treated HIV-infected patients with suppressed viraemia [50,51].
Atherosclerosis and cardiovascular disease are also closely related to immune activation and inflammation and have been shown to be highly prevalent among HIV-infected individuals, as is the case for many cardiovascular/metabolic risk factors (such as dyslipidemia, smoking, and central obesity) [52]. Immune activation and inflammation may therefore contribute to cognitive impairment in a direct manner, but also indirectly, by the association with vascular damage and cerebral small vessel disease.
Three factors were identified as risk factors for decreased cognitive performance, but not for cognitive impairment as a dichotomous outcome: having an above-normal waist-to-hip ratio, presence of depressive symptoms, and a lower nadir CD4+ cell count. This discrepancy might very well be explained by reduced statistical power when using cognitive impairment as a dichotomous outcome measure instead of cognitive performance as a continuous outcome measure.
In conclusion, our results indicate that reduced cognitive performance in HIV-1-infected men with sustained suppressed viraemia on cART is likely the result of a multifactorial process, in which not only HIV-associated factors, such as having experienced more severe immune deficiency, but also cardiovascular/metabolic factors, cannabis use, and depressive symptoms are key contributors. These are likely to gain increased importance as the population of people living with HIV continues to age.
Acknowledgements
The authors would like to thank Renée Baelde, Marleen Raterink, and Michelle Klein-Twennaar for their assistance in neuropsychological testing. They would also like to thank Joost Zandvliet for his assistance in statistical computing in R. They would also thank psychiatrists Ieke Visser and Eric Ruhé for their useful advice and support concerning capturing and interpreting depressive symptoms, and Tessa van der Knijff for monitoring, adjusting, and improving their neuropsychological dataset. They would thank Barbara Elsenga, Katherine Kooij, Rosan van Zoest, Aafien Henderiks, Jane Berkel, Sandra Moll, and Marjolein Martens for running the AGEhIV study program and capturing their data with such care and passion. They would also extend their thank to Yolanda Ruijs-Tiggelman, Lia Veenenberg-Benschop, Tieme Woudstra, Sima Zaheri, and Mariska Hillebregt at the HIV Monitoring Foundation for their contributions to data management, and Aafien Henderiks and Hans-Erik Nobel for their advice on logistics and organisation at the Academic Medical Center. They also 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, all Municipal Health Service Amsterdam personnel for their efforts to include the HIV-uninfected participants into the AGEhIV Cohort Study, and all study participants without whom this research would not be possible.
Contributions made by each of the authors: J.S. contributed to data collection, data analysis and interpretation, writing of all drafts of the manuscript, and was responsible for producing and submitting the final article. T.S. contributed to data collection, data analysis, and writing of the manuscript. F.W. contributed to the study design, data analysis and interpretation, and writing of the article. N.K. contributed to data collection, data interpretation, and writing of the article. M.C. contributed to data analysis and interpretation, and writing of the article. G.G. contributed to data analysis and interpretation, and contributed to writing of all drafts of the article. B.S. contributed to the study design, data analysis and interpretation, and contributed to writing of all drafts of the article. I.S. contributed to the study design, data collection, data interpretation, and writing of the article. M.P. contributed to the study design, data interpretation, and writing of the article. C.M. conceived the nested cognitive substudy, contributed to its design, to data interpretation, and writing of the article. P.P. contributed to the study design, data interpretation, and writing of the article. P.R. conceived the main cohort study and the nested cognitive substudy, contributed to both study designs, to data interpretation, and writing of all drafts of the article. Additional unrestricted scientific grants were received from Gilead Sciences, ViiV Healthcare, Janssen Pharmaceutica N.V., Bristol-Myers Squibb, Boehringer Ingelheim, and Merck & Co.
Study funding: 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).
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.
AGEhIV cohort study group members
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 (co-principal 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). Others 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, 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); B.A. Schmand, G.J. Geurtsen, 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).
Conflicts of interest
J.S. has received travel grants from Gilead Sciences, ViiV Healthcare, and Boehringer Ingelheim. T.S. has received travel grants from Boehringer Ingelheim. F.W. has received travel grants from Gilead Sciences, ViiV Healthcare, Boehringer Ingelheim, Abbvie, and Bristol-Myers Squibb. N.K. reports no conflicts of interest. M.C. has received travel grants from Boehringer Ingelheim. G.G. is a neuropsychological consultant for Cogstate clinical trials. B.S. receives royalties from Pearson and Hogrefe (test publishers). I.S. reports no conflicts of interest. M.P. received several (mainly noncommercial) grants, and received payment for several (mainly noncommercial) lectures. C.M. received a grant from NutsOhra Foundation, and received payment for lectures for Stryker. P.P. reports no conflicts of interest. 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. In addition, he serves on a scientific advisory board for Gilead Sciences, on a data safety monitoring committee for Janssen Pharmaceutica N.V., and chaired a company-organized scientific symposium for ViiV Healthcare, for which his institution has received renumeration.
References
1. Palella FJ, Delaney KM, Moorman AC, Loveless MO, Fuhrer J, Satten GA, et al.
Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. HIV Outpatient Study Investigators.
N Engl J Med 1998; 338:853–860.
2. 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.
3. Weber R, Ruppik M, Rickenbach M, Spoerri A, Furrer H, Battegay M, et al.
Decreasing mortality and changing patterns of causes of death in the Swiss HIV Cohort Study.
HIV Med 2013; 14:195–207.
4. Cysique LA, Brew BJ.
Prevalence of nonconfounded HIV-associated neurocognitive impairment in the context of plasma HIV RNA suppression.
J Neurovirol 2011; 17:176–183.
5. Winston A, Arenas-Pinto A, Stöhr W, Fisher M, Orkin CM, Aderogba K, et al.
Neurocognitive function in HIV infected patients on antiretroviral therapy.
PLoS One 2013; 8:e61949.
6. Schouten J, Cinque P, Gisslen M, Reiss P, Portegies P.
HIV-1 infection and cognitive impairment in the cART era: a review.
AIDS Lond Engl 2011; 25:561–575.
7. 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 Lond Engl 2007; 21:1915–1921.
8. 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 Lond Engl 2010; 24:1243–1250.
9. Heaton RK, Clifford DB, Franklin DR, 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.
10. Antinori A, Arendt G, Becker JT, Brew BJ, Byrd DA, Cherner M, et al.
Updated research nosology for HIV-associated neurocognitive disorders.
Neurology 2007; 69:1789–1799.
11. Su T, Schouten J, Geurtsen GJ, Wit FW, Stolte IG, Prins M, et al.
Multivariate normative comparison, a novel method for more reliably detecting cognitive impairment in HIV infection.
AIDS Lond Engl 2015; 29:547–557.
12. Heaton RK, Franklin DR, Ellis RJ, McCutchan JA, Letendre SL, Leblanc S, et al.
HIV-associated neurocognitive disorders before and during the era of combination antiretroviral therapy: differences in rates, nature, and predictors.
J Neurovirol 2011; 17:3–16.
13. Clifford DB, Ances BM.
HIV-associated neurocognitive disorder.
Lancet Infect Dis 2013; 13:976–986.
14. 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 Off Publ Infect Dis Soc Am 2014; 59:1787–1797.
15. Beck AT, Steer RA, Ball R, Ranieri W.
Comparison of Beck depression inventories -IA and -II in psychiatric outpatients.
J Pers Assess 1996; 67:588–597.
16. Beck A, Ward C, Mendelson M, Mochk J, Erbaugh J.
Assessment of depression: the depression inventory.
Arch Gen Psychiatry 1961; 4:561–571.
17. Broadbent DE, Cooper PF, FitzGerald P, Parkes KR.
The Cognitive Failures Questionnaire (CFQ) and its correlates.
Br J Clin Psychol Br Psychol Soc 1982; 21 (Pt 1):1–16.
18. Lawton M, Brody E.
Assessment of older people: Self-maintaining and instrumental activities of daily living.
Gerontologist 1982; 9:179–186.
19. Schmand B, Lindeboom j, van Harskamp F.
Dutch adult reading test. Lisse: Swets en Zeitlinger; 1992.
20. Huizenga HM, Smeding H, Grasman RPPP, Schmand B.
Multivariate normative comparisons.
Neuropsychologia 2007; 45:2534–2542.
21. Thames AD, Arbid N, Sayegh P.
Cannabis use and neurocognitive functioning in a nonclinical sample of users.
Addict Behav 2014; 39:994–999.
22. Cristiani SA, Pukay-Martin ND, Bornstein RA.
Marijuana use and cognitive function in HIV-infected people.
J Neuropsychiatry Clin Neurosci 2004; 16:330–335.
23. Lutge EE, Gray A, Siegfried N.
The medical use of cannabis for reducing morbidity and mortality in patients with HIV/AIDS.
Cochrane Database Syst Rev 2013; 4:CD005175.
24. Hazekamp A, Heerdink ER.
The prevalence and incidence of medicinal cannabis on prescription in The Netherlands.
Eur J Clin Pharmacol 2013; 69:1575–1580.
25. Lorius N, Locascio JJ, Rentz DM, Johnson KA, Sperling RA, Viswanathan A, et al.
Vascular disease and risk factors are associated with cognitive decline in the Alzheimer disease spectrum.
Alzheimer Dis Assoc Disord 2015; 29:18–25.
26. Raffaitin C, Féart C, Le Goff M, Amieva H, Helmer C, Akbaraly TN, et al.
Metabolic syndrome and cognitive decline in French elders: the Three-City Study.
Neurology 2011; 76:518–525.
27. Reijmer YD, van den Berg E, Dekker JM, Nijpels G, Stehouwer CDA, Kappelle LJ, et al.
Development of vascular risk factors over 15 years in relation to cognition: the Hoorn Study.
J Am Geriatr Soc 2012; 60:1426–1433.
28. Wright EJ, Grund B, Robertson K, Brew BJ, Roediger M, Bain MP, et al.
Cardiovascular risk factors associated with lower baseline cognitive performance in HIV-positive persons.
Neurology 2010; 75:864–873.
29. Bordier L, Doucet J, Boudet J, Bauduceau B.
Update on cognitive decline and dementia in elderly patients with diabetes.
Diabetes Metab 2014; 40:331–337.
30. Ganguli M, Fu B, Snitz BE, Hughes TF, Chang C-CH.
Mild cognitive impairment: incidence and vascular risk factors in a population-based cohort.
Neurology 2013; 80:2112–2120.
31. Fabbiani M, Ciccarelli N, Tana M, Farina S, Baldonero E, Di Cristo V, et al.
Cardiovascular risk factors and carotid intima-media thickness are associated with lower cognitive performance in HIV-infected patients.
HIV Med 2013; 14:136–144.
32. Valcour VG, Shikuma CM, Shiramizu BT, Williams AE, Watters MR, Poff PW, et al.
Diabetes, insulin resistance, and dementia among HIV-1-infected patients.
J Acquir Immune Defic Syndr 1999 2005; 38:31–36.
33. Sanz CM, Ruidavets J-B, Bongard V, Marquié J-C, Hanaire H, Ferrières J, et al.
Relationship between markers of insulin resistance, markers of adiposity, HbA1c, and cognitive functions in a middle-aged population-based sample: the MONA LISA study.
Diabetes Care 2013; 36:1512–1521.
34. Crichton GE, Elias MF, Buckley JD, Murphy KJ, Bryan J, Frisardi V.
Metabolic syndrome, cognitive performance, and dementia.
J Alzheimers Dis 2012; 30:S77–S87.
35. McCutchan JA, Marquie-Beck JA, Fitzsimons CA, Letendre SL, Ellis RJ, Heaton RK, et al.
Role of obesity, metabolic variables, and diabetes in HIV-associated neurocognitive disorder.
Neurology 2012; 78:485–492.
36. Weinstein G, Goldbourt U, Tanne D.
Angina pectoris severity among coronary heart disease patients is associated with subsequent cognitive impairment.
Alzheimer Dis Assoc Disord 2015; 29:6–11.
37. Reis JP, Launer LJ, Terry JG, Loria CM, Zeki Al Hazzouri A, Sidney S, et al.
Subclinical atherosclerotic calcification and cognitive functioning in middle-aged adults: the CARDIA study.
Atherosclerosis 2013; 231:72–77.
38. Becker JT, Kingsley L, Mullen J, Cohen B, Martin E, Miller EN, et al.
Vascular risk factors, HIV serostatus, and cognitive dysfunction in gay and bisexual men.
Neurology 2009; 73:1292–1299.
39. Anand S, Johansen KL, Tamura MK.
Aging and chronic kidney disease: the impact on physical function and cognition.
J Gerontol A Biol Sci Med Sci 2014; 69A:315–322.
40. Kalayjian RC, Wu K, Evans S, Clifford DB, Pallaki M, Currier JS, et al.
Proteinuria is associated with neurocognitive impairment in antiretroviral therapy treated HIV-infected individuals.
J Acquir Immune Defic Syndr 1999 2014; 67:30–35.
41. Cysique LA, Moffat K, Moore DM, Lane TA, Davies NWS, Carr A, et al.
HIV, vascular and aging injuries in the brain of clinically stable HIV-infected adults: a (1)H MRS study.
PloS One 2013; 8:e61738.
42. Nakamoto BK, Jahanshad N, McMurtray A, Kallianpur KJ, Chow DC, Valcour VG, et al.
Cerebrovascular risk factors and brain microstructural abnormalities on diffusion tensor images in HIV-infected individuals.
J Neurovirol 2012; 18:303–312.
43. 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 Basel Switz 2007; 24:236–241.
44. McIntyre RS, Cha DS, Soczynska JK, Woldeyohannes HO, Gallaugher LA, Kudlow P, et al.
Cognitive deficits and functional outcomes in major depressive disorder: determinants, substrates, and treatment interventions.
Depress Anxiety 2013; 30:515–527.
45. Malaspina L, Woods SP, Moore DJ, Depp C, Letendre SL, Jeste D, et al.
Successful cognitive aging in persons living with HIV infection.
J Neurovirol 2011; 17:110–119.
46. Cysique LA, Deutsch R, Atkinson JH, Young C, Marcotte TD, Dawson L, et al.
Incident major depression does not affect neuropsychological functioning in HIV-infected men.
J Int Neuropsychol Soc 2007; 13:1–11.
47. Ellis RJ, Badiee J, Vaida F, Letendre S, Heaton RK, Clifford D, et al.
CD4 nadir is a predictor of HIV neurocognitive impairment in the era of combination antiretroviral therapy.
AIDS Lond Engl 2011; 25:1747–1751.
48. Garvey L, Surendrakumar V, Winston A.
Low rates of neurocognitive impairment are observed in neuro-asymptomatic HIV-infected subjects on effective antiretroviral therapy.
HIV Clin Trials 2011; 12:333–338.
49. McCombe JA, Vivithanaporn P, Gill MJ, Power C.
Predictors of symptomatic HIV-associated neurocognitive disorders in universal healthcare.
HIV Med 2013; 14:99–107.
50. Brenchley JM, Price DA, Schacker TW, Asher TE, Silvestri G, Rao S, et al.
Microbial translocation is a cause of systemic immune activation in chronic HIV infection.
Nat Med 2006; 12:1365–1371.
51. Klatt NR, Chomont N, Douek DC, Deeks SG.
Immune activation and HIV persistence: implications for curative approaches to HIV infection.
Immunol Rev 2013; 254:326–342.
52. Zanni MV, Schouten J, Grinspoon SK, Reiss P.
Risk of coronary heart disease in patients with HIV infection.
Nat Rev Cardiol 2014; 11:728–741.
53. Letendre SL, Fitzsimons CA, Ellis RJ, Clifford D, Collier AC, Gelman B, et al.
Correlates of CSF viral loads in 1221 volunteers of the CHARTER Cohort. 17th Conference on Retroviruses and Opportunistic Infections, 16–19 February 2010, San Francisco, California; abstract 172.
54. Hildebrandt VH, Ooijendijk WTM, Hopman-Rock M.
Trendrapport Bewegen en Gezondheid 2004–2005. Leiden: TNO, 2007.
55. American Diabetes Association
Diagnosis classification of diabetes mellitus.
Diabetes Care 2012; 35 (Suppl 1):S64–S71.
56. Pickering TG, Hall JE, Appel LJ, Falkner BE, Graves JW, Hill MN, et al.
Recommendations for blood pressure measurement in humans: an AHA scientific statement from the Council on High Blood Pressure Research Professional and Public Education Subcommittee.
J Clin Hypertens Greenwich Conn 2005; 7:102–109.
