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 T 2. 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 . 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 T 2 statistic. The Hotelling's T 2 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 T 2 statistics were then transformed to a normal distribution by subtracting the lowest absolute Hotelling's T 2 statistic from all absolute Hotelling's T 2 statistics. This way the bimodal curve of the Hotelling's T 2 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.
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 T 2 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.
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 T 2 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).
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).
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 . Among HIV-infected individuals, depressive symptoms have also been associated with decreased cognitive function , although one study did not report an association between cognitive function and depressive symptoms .
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) . 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.
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.
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cognitive impairment; determinants; HIV-associated neurocognitive disorders; HIV infection; risk factors