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Select Neurocognitive Impairment in HIV-Infected Women: Associations with HIV Viral Load, Hepatitis C Virus, and Depression, but Not Leukocyte Telomere Length

  • Chantelle J. Giesbrecht,

    Affiliations Department of Psychology, Simon Fraser University, Burnaby, British Columbia, Canada, British Columbia Mental Health and Addictions Research Institute, Vancouver, British Columbia, Canada

  • Allen E. Thornton ,

    aethornt@sfu.ca

    Affiliations Department of Psychology, Simon Fraser University, Burnaby, British Columbia, Canada, British Columbia Mental Health and Addictions Research Institute, Vancouver, British Columbia, Canada

  • Clare Hall-Patch,

    Affiliations Oak Tree Clinic, BC Women's Hospital, Vancouver, British Columbia, Canada, CIHR Emerging Team in HIV Therapy and Aging (CARMA), Vancouver, British Columbia, Canada

  • Evelyn J. Maan,

    Affiliations Oak Tree Clinic, BC Women's Hospital, Vancouver, British Columbia, Canada, CIHR Emerging Team in HIV Therapy and Aging (CARMA), Vancouver, British Columbia, Canada

  • Hélène C. F. Côté,

    Affiliations CIHR Emerging Team in HIV Therapy and Aging (CARMA), Vancouver, British Columbia, Canada, Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada, Women's Health Research Institute, Vancouver, British Columbia, Canada

  • Deborah M. Money,

    Affiliations Oak Tree Clinic, BC Women's Hospital, Vancouver, British Columbia, Canada, CIHR Emerging Team in HIV Therapy and Aging (CARMA), Vancouver, British Columbia, Canada, Women's Health Research Institute, Vancouver, British Columbia, Canada, Department of Obstetrics & Gynecology, University of British Columbia, Vancouver, British Columbia, Canada

  • Melanie Murray,

    Affiliations Oak Tree Clinic, BC Women's Hospital, Vancouver, British Columbia, Canada, CIHR Emerging Team in HIV Therapy and Aging (CARMA), Vancouver, British Columbia, Canada, Women's Health Research Institute, Vancouver, British Columbia, Canada, Division of Infectious Disease, University of British Columbia, Vancouver, British Columbia, Canada

  • Neora Pick

    Affiliations Oak Tree Clinic, BC Women's Hospital, Vancouver, British Columbia, Canada, CIHR Emerging Team in HIV Therapy and Aging (CARMA), Vancouver, British Columbia, Canada, Women's Health Research Institute, Vancouver, British Columbia, Canada, Division of Infectious Disease, University of British Columbia, Vancouver, British Columbia, Canada

Abstract

Background

Through implementation of combination antiretroviral therapy (cART) remarkable gains have been achieved in the management of HIV infection; nonetheless, the neurocognitive consequences of infection remain a pivotal concern in the cART era. Research has often employed norm-referenced neuropsychological scores, derived from healthy populations (excluding many seronegative individuals at high risk for HIV infection), to characterize impairments in predominately male HIV-infected populations.

Methods

Using matched-group methodology, we assessed 81 HIV-seropositive (HIV+) women with established neuropsychological measures validated for detection of HIV-related impairments, as well as additional detailed tests of executive function and decision-making from the Cambridge Neuropsychological Test Automated Battery (CANTAB).

Results

On validated tests, the HIV+ women exhibited impairments that were limited to significantly slower information processing speed when compared with 45 HIV-seronegative (HIV−) women with very similar demographic backgrounds and illness comorbidities. Additionally, select executive impairments in shifting attention (i.e., reversal learning) and in decision-making quality were revealed in HIV+ participants. Modifiers of neurocognition in HIV-infected women included detectable HIV plasma viral load, active hepatitis C virus co-infection, and self-reported depression symptoms. In contrast, leukocyte telomere length (LTL), a marker of cellular aging, did not significantly differ between HIV+ and HIV− women, nor was LTL associated with overall neurocognition in the HIV+ group.

Conclusions

The findings suggest that well-managed HIV infection may entail a more circumscribed neurocognitive deficit pattern than that reported in many norm-referenced studies, and that common comorbidities make a secondary contribution to HIV-related neurocognitive impairments.

Introduction

Despite advances in HIV antiretroviral treatment (ART), namely the advent of combination ART (cART), HIV infection continues to be linked to deleterious functional and structural consequences for brain parenchyma [1][8]. Estimates indicate that as many as 50% of HIV+ individuals display some degree of neurocognitive dysfunction when impairment is derived from comparisons with normative performance standards (e.g., [9][11]). Nevertheless, a recent meta-analysis revealed lesser attentional, motor, and executive skill impairments in HIV+ individuals treated with cART relative to monotherapy [12]. Although impairment profiles vary amongst HIV+ individuals [13], deficits in speed of information processing [14][16], fine motor speed and dexterity [17], [18], aspects of learning and memory [19][23], and multiple domains of executive functioning [17], [24][30] are commonly identified. Impairment estimates appear to be attenuated when ascertained from studies in which HIV+ samples were “matched” or comparable to the control group, with respect to background demographics, lifestyle and risk variables (e.g., substance use, sexually transmitted diseases, etc.) [31], [32], or when these variables were statistically controlled [33].

A limitation of the existing literature examining neurocognitive functioning in HIV is the underrepresentation of women. Relative to men, women living with HIV are more likely to reside in marginalized settings, be less educated, and have a greater frequency of substance use including intravenous drug use (IDU) [34][37]. Further, compared to HIV+ men, HIV+ women are less likely to receive recommended health care. Poorer quality of care in women is associated with younger age, substance use/IDU, lower annual income, and low trust in care providers [38], [39]. These factors may directly or indirectly modify the HIV-related neurocognitive deficits observed.

In addition to describing the types and patterns of neurocognitive impairment in HIV+ populations, researchers continue to explore the associated risk factors. Several factors directly related to HIV and its treatment, such as CD4 nadir count [11], [29], [40] and detectable HIV plasma viral load [17], [41], [42], have been associated with neurocognitive impairment in some, but not all studies [10], [43][46]. Further, non-specific comorbid conditions may worsen neurocognitive impairment, including depression [42], [47], co-infection with hepatitis C virus (HCV) [42], [48][50], and substance use [51][53].

Further, given the aging of HIV+ cohorts, the potential for the HIV virus and/or associated factors to accelerate age-related cognitive decline has become a research imperative. Recent evidence reveals higher rates of age-associated illnesses, including cardiovascular disease, hypertension, and diabetes in HIV+ populations [54]. Whether HIV-related neurocognitive impairments are further compromised by the complex processes underlying aging has received only limited attention. Cellular aging is marked by a shortening in telomere length (TL), which has been used as a biomarker of aging. Consequently, TL in persons with HIV may reflect individual variation in cellular processes that extend beyond those typically conferred in the course of “normal” aging. Recent investigations report shorter leukocyte TL (LTL) in individuals with an HIV+ status [55][57], potentially implicating accelerated biological aging in HIV+ individuals. Shortened TL in immune cell subsets has been associated with HIV infection [58][62] and our group has reported that shorter LTL is associated with detectable viral load in HIV+ children [63].

Moreover, shortened TL reportedly increases neurocognitive impairment and risk of dementia in non-HIV populations [64][66] and, as such, may be a marker for atypical age-related cognitive decline [67][69]. One prior investigation of HIV+ women determined that shorter LTL was associated with select neurocognitive dysfunction involving learning [56]. Thus, shortened TL, beyond that expected for a given age, may predict neurocognitive functioning in persons with HIV and ultimately suggest accelerated neurocognitive aging.

The present study provides a contemporary estimate of the nature and extent of neurocognitive dysfunction in HIV+ women, relative to carefully matched HIV-seronegative (HIV−) women with similar social determinants and neurocognitive risk factors (e.g., income, HCV, etc.). Our assessment battery included measures that have been previously validated for use in HIV populations [70]. Additionally, a detailed and comprehensive examination of executive functioning was conducted to isolate core executive dysfunctions, which are generally known to be critical to real-world adaptation [71][73]. We also investigated potential modifiers of neurocognitive dysfunction, including HIV disease- and treatment-related factors, and comorbid conditions. Finally, we assessed LTL in our study participants, to explore any potential association with neurocognition, after controlling for relevant covariates.

Methods

Ethics Statement

Written informed consent was obtained from each participant. The study was approved by the Institutional Review Boards of both the University of British Columbia and Simon Fraser University (ethics reference numbers: H09-02867 & 2010s0464, respectively), and was conducted in accordance with the Helsinki Declaration (59th WMA General Assembly, Seoul, 2008).

Participants

One hundred and twenty-six (126) women were administered neurocognitive batteries between August 2010 and May 2012. All participants had adequate English language fluency for the purpose of valid psychometric testing. Exclusion criteria included; age <30 years, head injury with loss of consciousness >10 minutes, being currently pregnant, serious mental illness (e.g., bipolar disorder or schizophrenia), or known central nervous system pathology, including progressive multifocal leukoencephalopathy, cancer affecting the brain, neurosyphilis, active cytomegalovirus infection, multiple sclerosis, stroke, and seizures/epilepsy. Eighty-one (81) HIV+ women were recruited from the Oak Tree Clinic in Vancouver, British Columbia (BC), a provincial multidisciplinary HIV clinic primarily for women and children. Forty-five (45) HIV− control women were recruited throughout the Lower Mainland of BC via advertisements strategically posted in various venues, including women's health clinics and Craigslist. Control participants were tested for HIV antibody upon recruitment in the study unless they had a negative test in the past 3 months with no other illness during this time period. All neurocognitive testing was conducted at the BC Women's Hospital and Health Centre. Participants received a modest honorarium for their participation.

Material and Procedures

Medical, clinical, and substance use evaluations.

General medical history was obtained via self-report for all participants. For HIV+ participants, HIV disease duration (estimated from date of HIV diagnosis) and ART history were retrieved from medical chart and clinic pharmacy records. Both HIV plasma viral load (copies/mL) and CD4 count (cells/µL) were obtained from blood work collected within 5 months of neurocognitive testing. HCV seropositivity (HCV+) was self-reported by control participants and retrieved from medical chart for HIV+ participants. For participants identified as being HCV seropositive, a follow-up qualitative PCR (qPCR) on plasma collected on study visit day was performed at the BC Centre for Disease Control to ascertain whether HCV infection was active.

Substance use history was obtained from all participants with a self-report questionnaire. To summarize substance use information five different ‘Substance Use’ variables were developed for the different substance use histories assessed (i.e., amphetamines, cocaine, crack, heroin, and marijuana). For every participant each substance was dichotomized into lifetime minimal use (monthly or rarely/never) or lifetime severe use (daily or weekly). A ‘Lifetime Substance Use’ variable was created, and used to represent substance use in subsequent statistical analyses, by dichotomizing participants into those who never used any substance and those who reported use of at least one substance in their lifetime. Recency of last substance use was also determined using a ‘Day of Evaluation’ questionnaire, in which participants self-reported whether they used various substances within 24 hours of cognitive testing.

To assess LTL, DNA was extracted from peripheral blood collected on the same day as neurocognitive testing. Relative mean LTL was measured by monoplex qPCR as described previously [74].

Psychometric assessments.

English language fluency was assessed with a brief self-report questionnaire of subjective language preferences for various activities (e.g., reading, speaking, etc.). The Center for Epidemiologic Studies Depression Scale (CES-D) [75] was used to evaluate current levels of depression symptoms. To determine group comparability on premorbid intellectual abilities, the Wechsler Test of Adult Reading (WTAR) [76] was administered. For the vast majority of participants, the WTAR Word Reading plus demographic-predicted Full Scale IQ (FSIQ) was used as the premorbid estimate. For five HIV+ participants and one HIV− participant for whom English was a second language, but who were nonetheless fluent, the WTAR demographic-only predicted FSIQ was used.

Neurocognitive assessment.

All participants completed a comprehensive neuropsychological battery. Test formats fell into two categories: conventional paper and pencil tests validated in the existent literature [70] and the computerized Cambridge Neuropsychological Test Automated Battery (CANTAB) [77]. Using the conventional tests, we evaluated fine motor speed and dexterity (Grooved Pegboard Test) [78], learning and memory (Hopkins Verbal Learning Test-Revised; HVLT-R) [79], speed of information processing (Symbol Search and Coding; WAIS-IV) [80], and working memory (Letter-Number Sequencing; WAIS-IV) [80]. Along with the HVLT-R, learning and memory were further examined using the Paired Associates Learning (PAL) subtest from the CANTAB, a computerized visual memory test that evaluates conditional learning of pattern-location associations. The number of patterns recalled after a single viewing and the total number of errors made were assessed.

Additionally, the CANTAB was used in conjunction with Letter-Number Sequencing to comprehensively evaluate executive abilities (Table 1). Several “core” (e.g., attention, working memory, information updating and monitoring, response inhibition, attentional set-shifting) and “compiled” (e.g., decision-making, planning) domains of executive functioning were targeted. This battery of tests captures the organization taxonomy and breadth of executive functions [81][83].

To quantify overall level of neurocognition a standardized composite z-score index (Composite Cognitive Index; CCI) was calculated. Briefly, z-scores were computed for 13 non-redundant neurocognitive outcome measures, among all the tests administered, on the basis of the mean and standard deviation of the HIV− group [84], [85], using transformed data when required. For each HIV+ and HIV− participant the mean of their individual z-scores was generated and used as a composite (overall) index of neurocognitive performance. This in turn was employed to ascertain overall group differences in neurocognition as well as in subsequent analyses of potential modifiers of neurocognition.

Statistical Analyses

Group Comparisons.

All comparisons made between HIV+ and HIV− groups were conducted on raw neurocognitive data and on the CCI. For normally distributed data, independent t-tests were employed. For a subset of CANTAB outcome measures, mathematical transformations (e.g., square root and logarithm10) failed to adequately normalize the distributions; therefore, Mann-Whitney U nonparametric tests were conducted on the raw, untransformed scores. Effect sizes (ES) for group comparisons were also calculated for each neurocognitive outcome measure. Cohen's d was employed for normally distributed data (corresponding to ES of small = .2, medium = .5, large = .8) [86]. For non-normal distributions, comparable ES were calculated [87] based upon the proportion of the HIV− group that fell below (i.e., worse performance) the median for the HIV+ group (γc < Median (γe); where c = HIV− group and e = HIV+ group). Despite the fact that the distributions for these select scores were non-normal, the proportions were then converted into z-values appropriate for abnormal distributions, which estimates the median performance of the HIV+ group relative to the control distribution (e.g., z-value of −1.0 represents the 16th percentile of the control group distribution) [87].

Normative-Referenced Scores.

For conventional neurocognitive measures (i.e., Grooved Pegboard, HVLT-R, Symbol Search, Coding, LNS) performance of the HIV+ group was further inspected in the context of published normative data [78][80] using the following procedures. First, each HIV+ participant's raw score was converted to its corresponding age-corrected standard score based upon the published norm-referenced data. Next, for each measure, the arithmetic mean for the HIV+ group was computed, which represents the HIV+ group's performance on each measure relative to the published normative sample. Lastly, because scores are given in various standardized units (e.g., t-scores, scaled scores, etc.), the HIV+ group means for each measure were converted to a common z-score metric (mean = 0 and standard deviation = 1.0 with comparable percentiles to Cohen's d). These normative-referenced z-scores represent the deviation of the HIV+ group from that of the published normative standardization sample on the conventional neurocognitive measures.

Neurocognitive Correlates.

In the HIV+ group partial correlations (including point biserial partial correlations) [88] controlling for age and education were used to explore the associations between discrete aspects of neurocognition, as indexed by raw scores, and the following: 1) disease- and treatment-related factors (CD4 nadir cell count, current CD4 cell count, current detectable HIV plasma viral load, duration since HIV diagnosis, years on ART), 2) comorbid conditions (depression, active HCV infection, lifetime substance use), and 3) LTL. Exceptions included employing Spearman's rho for scores from the Intra-Extra Dimensional Set Shift (IED) test (after confirming no correlation with age and education) as transformations failed to adequately normalize the distributions. Additionally, prior to running analysis, several data modifications were employed to ensure normal distributions. For Grooved Pegboard, time taken with dominant and non-dominant hand was combined for a total time score. For RVP (A′) transformed data (cubed) was used. For the decision-making score, Quality of Decision-Making and Risk Adjustment scores from the Cambridge Gambling Task were combined (raw scores were z-score transformed and summed).

LTL Investigations.

An analysis of covariance (ANCOVA) was used to compare LTL differences between HIV+ and HIV− groups. Age was used as a covariate in the model, given that it was marginally associated with LTL in the entire sample (r = −.16; p = .07).

Prediction of Overall Cognition.

Finally, two hierarchical linear regression analyses (HLRA) were conducted separately in the HIV+ and HIV− samples to investigate the contribution of demographic factors, disease- and treatment-related factors, comorbid conditions, and LTL to overall neurocognition as measured by the CCI. A separate analysis for each sample was conducted given that HIV disease- and treatment-related factors could only be investigated in the HIV+ group, and the apparent differential relationships observed for each sample in the associations between clinical and demographic variables and CCI. Based upon zero-order correlations potential independent variables were pre-screened prior to inclusion into the model and only those that shared at least 5% of their variance with CCI were entered. For both samples the pre-screened variables considered were education, depression, active HCV infection, and lifetime substance use. In the HIV+ sample we also pre-screened CD4 nadir cell count, current CD4 cell count, detectable HIV viral load, duration since HIV diagnosis, and years on ART. Age and LTL were included in both analyses to evaluate the extent to which LTL (entered in the final block) contributed to overall neurocognition beyond that of age and the pre-screened variables. All statistical analyses were conducted with IBM SPSS Statistics V. 20.0 (IBM Corporation, Armonk, NY).

Results

Group Comparability

HIV+ and HIV− participants were well matched on age, premorbid intellectual functioning (WTAR FSIQ), education, ethnicity, and income, and were comparable on several comorbid conditions that are potential modifiers of impairment, including depressive symptoms, HCV serostatus, and lifetime substance use (see Table 2). The only exception was a significantly higher frequency (Fisher's exact test: p = .01) of self-reported recent cocaine use in the HIV− group (n = 7/45) compared to the HIV+ group (n = 2/81). However, given that the half-life of cocaine is approximately one hour [89], [90], and that the mean reported time since recent use in the entire group was approximately 12 h (M = 12.69 h; SD = 3.34 h) this was not likely to confound the results (see review [91]). Descriptive statistics for disease- and treatment-related variables in the HIV+ group are also shown in Table 2.

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Table 2. Demographic and clinical characteristics of HIV+ and HIV− participants.

https://doi.org/10.1371/journal.pone.0089556.t002

HIV+ and HIV− Differences in Discrete Aspects of Neurocognition

Table 3 reveals select neuropsychological differences between the HIV+ and HIV− participants. Specifically, HIV+ women exhibited slower visual scanning/discrimination speed (e.g., Symbol Search; t(123) = −2.21, p = 0.03), which was the only conventional test detecting impairment. On the CANTAB measures, moderate to large impairments in select executive skills were revealed. Specifically, on the IED task, HIV+ participants made significantly more errors on the pre-extradimensional shift stages (U = 1277.50, Z = −2.62, p = 0.009), which reflected greater reversal errors (U = 1274.50, Z = −2.72, p = 0.007), and a trend towards greater discrimination errors (U = 1436.00, Z = −1.79, p = 0.072). Note that these analyses included data from subsets of participants for whom adjustments were made to error scores for the stages that were not completed (see Table 1 for score adjustment details, also see [92] for similar adjustments). Nonetheless, the results were maintained for participants who completed all relevant IED stages: pre-extradimensional shift stage errors (U = 982.00, Z = −2.08, p = .037), reversal errors (U = 996.50, Z = −2.10, p = .035), and discrimination errors (U = 1280.50, Z = −1.68, p = .093). Reversal stage errors suggested a core deficit in attentional set-shifting; specifically, the HIV+ group suffered degradation in shifting to previously irrelevant stimuli within the context of a rule change. Additionally, the HIV+ group demonstrated significantly poorer quality of decision-making on the Cambridge Gambling Task (CGT; U = 1379.50, Z = −2.09, p = 0.04). That is, they more often gambled on the less likely outcome. There were no significant group differences in fine motor speed and dexterity, learning and memory, or the remaining sub-domains of executive function. Lastly, the groups did not significantly differ on the CCI (see Table 3 notation for measure that constitute the CCI).

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Table 3. Comparison of neurocognitive performance between HIV+ and HIV- groups.

https://doi.org/10.1371/journal.pone.0089556.t003

HIV+ Participants' Neurocognition Relative to Normative-Referenced Standards

When the HIV+ group's neurocognitive performances were indexed to published normative-referenced standards, the magnitude of impairments appeared much greater than when deficits were estimated on the basis of comparisons to a well-matched control group (see Table 3). This pattern emerged on tests of fine motor speed and dexterity (Grooved Pegboard), learning and memory (HVLT-R), information processing speed (Coding), and working memory (LNS). The only exception was for the task involving visual scanning/discrimination speed (Symbol Search) where the magnitude of impairment was comparable when either the matched control group (Cohen's d = −0.41) or published normative data (z-score = −0.42) were employed.

Associates of Discrete Aspects of Neurocognition in HIV+ Participants

The relationship between disease- and treatment-related variables and neurocognition in the HIV+ group are shown in Table 4. Lower current CD4 count was significantly associated with more errors on reversal learning, but none of the other neurocognitive abilities. Detectable, or non-suppressed, HIV viral load (>200 copies/mL) was related to poorer neurocognitive functioning. This result was observed in overall neurocognition (CCI) and among specific domains including information processing speed, learning, working memory, and information updating and monitoring. Further analysis comparing the virally suppressed group (i.e., viral load ≤200 copies/mL; n = 62) to the detectable viral load group (n = 19) revealed a significant difference in overall neurocognitive functioning (CCI; t(79) = 3.0, p = .004), with the detectable virus group performing significantly worse (M = −0.89, SD = 0.97) relative to the virally suppressed group (M = −0.08, SD = 1.04). A longer duration since HIV diagnosis was associated with better learning and memory, a larger working memory capacity, and selectively faster information processing speed (e.g., Coding). The lifetime number of years treated with ART, which was confounded with duration since HIV diagnosis (r = .805, p<.001), was similarly related to better learning, and selectively faster information processing speed.

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Table 4. Correlations between neurocognitive performance and disease- and treatment-related variables, and comorbid conditions in the HIV+ group.

https://doi.org/10.1371/journal.pone.0089556.t004

The relationship between comorbid conditions and neurocognition in the HIV+ group are also reported in Table 4. Greater depressive symptoms were significantly associated with worse neurocognitive functioning overall (CCI) and among specific domains; including poorer information updating and monitoring, worse discrimination of stimuli, and lesser planning capacity. Additionally, active HCV infection was associated with poorer overall neurocognition (CCI) and specifically with slowing of motor and information processing speed, poorer attention, and reduced working memory capacity and planning behaviour. Also, heavy lifetime substance use was selectively associated with decreased response inhibition. Finally, no significant partial correlations were identified between discrete aspects of neurocognition and LTL in the HIV+ group (see Table 4).

HIV+ and HIV− Differences in LTL

Prior to conducting the ANCOVA we identified one LTL value in the HIV+ group that was a significant outlier and adjusted it accordingly [93]. Further, two HIV+ cases and one HIV− case had missing LTL values and were excluded from this analysis and subsequent analyses involving LTL. The ANCOVA comparing HIV+ and HIV− groups (see Table 2) revealed no significant group differences in LTL (F (1, 120) = 0.25, p = .62), after accounting for age (F (1, 120) = 2.57, p = .11).

Modifiers of Overall Neurocognition in HIV+ Participants

The following HLRA results were based upon entering four blocks of variables in the prediction of overall neurocognition of HIV+ participants, after pre-screening (i.e., those sharing at least 5% of variance with CCI) for possible predictors. In the first block, demographic variables were entered, followed by variables capturing comorbid conditions, subsequently HIV disease- and treatment-related variables were entered, and finally LTL was entered. On the first block, education and age together explained 20.6% of the variance in CCI (F(2, 76) = 9.86, p<.001). On block two, the comorbid factors of depression and active HCV infection explained an additional 11.4% of the variance (F(2, 74) = 6.19; p = .003). Subsequently, detectable HIV viral load, entered on block three, explained a further 7.0% of the variance (F(1, 73) = 8.34, p = .005). On the final block LTL was entered, but it did not account for a significant amount of additional variance in CCI (F(1, 72) = 0.19, p = .67). The final model was significant (F(6, 72) = 7.71, p<.001), accounting for 39.1% (34% adjusted) of the variance in CCI. In addition to LTL (β = .04, p = .67), the full model indicated that age (β = −.13, p = .19) was not a significant predictor of CCI, but education (β = .34, p = .001), depression (β = −.21, p = .026), HCV infection (β = −.28, p = .004), and detectable HIV viral load (β = −.26 p = .009) were significant.

Modifiers of Overall Neurocognition in HIV− Participants

For completeness, a similar HLRA approach was adopted in the investigation of overall neurocognition in HIV− participants. Pre-screened variables sharing at least 5% variance with CCI included education and lifetime substance use. Age and LTL were also included in the model. Consequently, the HLRA was constructed by entering three variable blocks in the following order: 1) age and education, 2) lifetime substance use, and 3) LTL. Age and education explained 51.7% of the variance in CCI (F(2, 41) = 21.91; p<.001). The addition of lifetime substance use contributed minimally (<1%) to the variability of CCI (F(1, 40) = .32; p = .58). Lastly, LTL explained an additional 5.0% of the variance in overall neurocognition (F(1, 39) = 4.51; p = .04). The full model was significant (F(4, 39) = 12.93, p<.001), explaining 57% (53% adjusted) of the variance in overall neurocognition (CCI). The model indicated that lifetime substance use (β = .14, p = .31) was not a significant predictor of CCI, but age (β = −.23, p = .04), education (β = .69, p<.001), and LTL (β = .23 p = .04) were significant.

Discussion

Compared to existent research, the current findings are fairly unique in revealing relatively select neurocognitive impairments in HIV+ women who were well matched to controls on age, premorbid intellectual abilities, education, ethnicity, and household income, and on other potential confounds (e.g., depressive symptoms, active HCV infection, and lifetime substance use). The current study revealed impairments that were limited to slowing of information processing and deficits in executive function. Decreased speed of information processing is a well-established finding within HIV+ populations [14][16]; however; in contrast to most prior reports, this was the only significant deficit detected on conventional tests. Nonetheless, by deploying computerized (CANTAB) measures fairly novel to the HIV literature (see [18], [94] for additional use of select subtests and scores in HIV+ samples), the findings extended our understanding of core [82], [95] HIV-related executive impairments in women.

Functionally, HIV+ women had difficulty responding to previously irrelevant stimuli (IED Stages 2, 5, 7) despite feedback suggesting that the stimulus was now relevant to success. This executive impairment pattern, indicative of a reversal learning deficit, has been associated with activations of the ventral prefrontal cortex [96][99]. In contrast, HIV+ women showed only marginal trends towards impairment in rule acquisition (IED discrimination stages). Further, they were relatively successful in shifting attention to a novel exemplar of a previously unrewarded dimension (IED extradimensional shift errors; Stage 8), which is analogous to the set shifting dimension of the Wisconsin Card Sorting Test and related to functions of the dorsolateral prefrontal cortex [96], [100]. The fairly isolated reversal learning impairment in HIV+ women represents a reduced response to negative feedback [101], manifesting as a decreased ability to apprehend signals suggesting that environmental contingencies have changed.

Further, HIV-related impairments in decision-making abilities were isolated to deficits in the quality of their decision-making (CGT), such that HIV+ women more often chose the option that was less likely to be correct, reflective of poorer probabilistic judgment. We did not find group differences in proportion of points placed at risk across different odds ratio trials (CGT – risk adjustment). Both groups equivalently risked a higher proportion of their points when the odds were more in their favor. The lack of greater “risky decision-making” in HIV+ women diverges from prior reports of riskier decisions on the Iowa Gambling Task (IGT) in mixed-gender [26] and male-only HIV+ samples [25]. Along with possible gender differences, it is plausible that task-specific demands explain this discrepancy. The CGT captures simpler core cognitive processes as compared to more complex processes assessed by the IGT, such that the CGT provides more tangible and pertinent risk-exposure information on each gambling trial and minimizes the inter-trial learning component.

Overall, the rather limited array of HIV-related impairments detected in this study appears to diverge from the existing literature. Two characteristics of the current study may provide context for this apparent discrepancy. First, the narrower array of deficits we observed on conventional tests may reflect our comparator group approach, as we chose not to rely on normative data. In contrast, approximately 70% of all studies published in the last 5 years have utilized published normative data to characterize cognitive functioning in HIV+ samples (results available upon request). These studies have generally found greater and more widespread impairment patterns. Indeed, when neurocognitive performance of the current HIV+ sample was indexed to normative data, HIV-related impairment appeared magnified and included impairments in learning, memory and working memory as well as deficits in fine motor speed and dexterity. These normative-based findings, however, fail to account for important comorbidities that may negatively modify neurocognition (see below). By using a matched comparator sample approach, we estimated impairments that are apt to be more exclusively related to HIV+ viral status and/or its treatment.

Secondly, the more circumscribed pattern of impairments may reflect differences in the manifestations of HIV-related neurocognitive deficits in a female sample. The few prior studies that have examined women only also detected relatively limited impairments on conventional tests, which often were restricted to psychomotor and processing speed deficits [33], [51], [84]. Consequently, findings from men-only and mixed samples may not directly relate to women-only samples [102]. Ultimately, establishing normative data relevant to specific HIV+ sub-populations appears to be crucial, as suggested by others [31], [33].

The current research also provided further insights into the differential impact of various potential modifiers of neurocognitive functioning, including HIV-related factors and secondary comorbidity. Notably, we determined that suffering from depression and active HCV infection were related to worse neurocognition and accounted for a substantial portion of overall neurocognition (i.e., CCI) observed in the HIV+ group. Further, the presence of detectable HIV viral load predicted additional neurocognitive attenuation and accounted for significant performance variability beyond comorbidity. This suggests that inherently occurring confounds of HIV infection account for some, but not all, of the neurocognitive variation. Interestingly, these same comorbidities did not account for significant neurocognitive performance variability in HIV− individuals.

To our knowledge our study is only the second (after Malan- Müller et al. 2013) [56], to examine the relationship between LTL and neurocognition in HIV+ women. Shorter LTL has been associated with HIV infection [55][57] and linked to worse neurocognitive function in both normal aging [67][69] and non-HIV related dementia [64][66]. Nonetheless, we found no difference in the LTLs of the HIV+ versus HIV− groups in this sample. Further, in contrast to Malan-Müller et al. (2013) [56], we did not identify significant associations between neurocognition (both overall and on select tests) and LTL in the HIV+ participants. Notably, the present sample was better educated and had much higher rates of cART than the sample investigated by Malan-Müller and colleagues.

Interestingly, shorter LTL was associated with poorer overall cognition in HIV− participants, after accounting for variance explained by demographic variables and lifetime substance use. Perhaps LTL is not a uniform marker of cellular aging in select populations or when under certain influences [103]. Indeed, it is plausible that in addition to HIV status, our groups differed on other unidentified factors that alter TL, such as father's age at birth [57], amount of physical activity [104], or estrogen levels [105]. These factors may be differentially associated with the groups; thus, obscuring the associations between LTL and neurocognition.

Like all research, several limitations should be noted. The current study was not designed to longitudinally track the effects of cART on neurocognition. This line of inquiry may be important as certain medications have greater brain penetration and potentially greater protective and beneficial effects [106]. Future longitudinal studies may also clarify the unexpected finding that select aspects of neurocognition were related to a longer history of HIV and ART treatment, a finding that has also been previously reported [42]. Individuals who are more adherent to longer-term treatment may show moderation of their neurocognitive impairment. Alternatively, sample ascertainment factors may be operating, such that a subset of women in the sample with higher cognitive functioning who received regular, long-term, services within the clinic may have been disproportionately represented.

The self-report nature of substance use in the entire sample could have been improved through urine drug toxicology screens. Like most past studies we did not include standardized protocols addressing substance abuse and dependence; however, we did capture a history of past and recent use. Another limitation was in using self-report in the identification of HCV status in the control group, with subsequent qPCR testing of the identified control participants. Self-report of HCV status in controls may represent a confound in the group comparisons. This is unlikely, however, in that several control participants would have to be unaware of their HCV infection, given the slightly lower, yet non-significant, difference in the percentage of control participants reporting HCV (24.4%) relative to the percentage of participants in the HIV+ group with HCV (34.6%).

It is also possible that certain characteristics of our HIV+ sample may account for the findings we observed, including the limited neurocognitive deficits detected. These characteristics include exclusively enrolling Canadian women being treated in a specialty clinic for HIV (note that the HIV+ women were fairly representative of the Canadian HIV+ population), the fact that the majority of HIV+ women were currently virally suppressed, and exclusion of women with moderate to severe head injuries. These characteristics may influence the generalizability of our study to other research contexts.

In conclusion, we found circumscribed neurocognitive impairments in a sample of HIV+ women that was well-matched to a comparison group, namely slower information processing, poorer reversal learning, and suppressed quality of decision-making. The combined deficiencies in quality of decision-making and reversal learning may further indicate impaired behavioural adaptation [107], which involves incorporating relevant information from the environment to modify behaviour in a more beneficial manner. The neurocognitive impairments detected in this study could have important implications for day-to-day functioning. The slowed information processing reflects a reduction in the speed with which new information can be processed and may reflect longer times to complete common everyday tasks [108], [109], as well as possibly affecting how well patients process new information during medical appointments. This deficit, in combination with reduced reversal learning, may make completing instrumental activities of daily living more cumbersome [92], [97], such as learning and maintaining new medication regimens [46], [110]. Continued research into the mechanisms that maintain HIV-relevant neurocognitive dysfunction in the cART era, along with the underlying brain circuitry, may provide targets for interventions that could have meaningful consequences in the real world. Additionally, this research continues to highlight the importance of evaluating depression and HCV in HIV+ women, suggesting that implementation of medication for HCV and/or treatment for depression may mitigate neurocognitive impairment, and consequently further reduce deficits observed in day-to-day activities (e.g., work, driving ability, etc.).

Acknowledgments

We wish to thank Rebecca Graham for her help with the project including participant enrollment and obtaining supplemental data, DeAnna Zanat for help with standardizing the substance use data, and Beheroze Sattha for supplemental data entry. We also thank Dr. Mel Krajden for his assistance with the HCV RNA qualitative testing.

Author Contributions

Conceived and designed the experiments: NP AET HCFC EJM DMM MM. Performed the experiments: CHP CJG. Analyzed the data: CJG AET. Contributed reagents/materials/analysis tools: HCFC. Wrote the paper: CJG AET. Contributed to editing the manuscript: HCFC EJM NP MM DMM.

References

  1. 1. Patel S, Kolson D, Glosser G, Matozzo I, Ge Y, et al. (2002) Correlation between percentage of brain parenchymal volume and neurocognitive performance in HIV-infected patients. Am J Neuroradiol 23: 543–549.
  2. 2. Paul R, Cohen R, Navia B, Tashima K (2002) Relationships between cognition and structural neuroimaging findings in adults with human immunodeficiency virus type-1. Neurosci Biobehav Rev 26: 353–359 10.1016/S0149-7634(02)00006-4.
  3. 3. Anthony I, Ramage S, Carnie F, Simmonds P, Bell J (2005) Influence of HAART on HIV-related CNS disease and neuroinflammation. J Neuropathol Exp Neurol 64: 529–536.
  4. 4. Thompson P, Dutton R, Hayashi K, Toga A, Lopez O, et al. (2005) Thinning of the cerebral cortex visualized in HIV/AIDS reflects CD4(+) T lymphocyte decline. Proc Natl Acad Sci U S A 102: 15647–15652 10.1073/pnas.0502548102.
  5. 5. Chang L, Andres M, Sadino J, Jiang CS, Nakama H, et al. (2011) Impact of apolipoprotein E epsilon 4 and HIV on cognition and brain atrophy: Antagonistic pleiotropy and premature brain aging. Neuroimage 58: 1017–1027 10.1016/j.neuroimage.2011.07.010.
  6. 6. Ances BM, Ortega M, Vaida F, Heaps J, Paul R (2012) Independent effects of HIV, aging, and HAART on brain volumetric measures. J Acquir Immune Defic Syndr 59: 469–477 10.1097/QAI.0b013e318249db17.
  7. 7. Becker JT, Maruca V, Kingsley LA, Sanders JM, Alger JR, et al. (2012) Factors affecting brain structure in men with HIV disease in the post-HAART era. Neuroradiology 54: 113–121 10.1007/s00234-011-0854-2.
  8. 8. Towgood KJ, Pitkanen M, Kulasegaram R, Fradera A, Kumar A, et al. (2012) Mapping the brain in younger and older asymptomatic HIV-1 men: Frontal volume changes in the absence of other cortical or diffusion tensor abnormalities. Cortex 48: 230–241 10.1016/j.cortex.2011.03.006.
  9. 9. Heaton RK, Clifford DB, Franklin DR, Woods SP, Ake C, et al. (2010) HIV-associated neurocognitive disorders persist in the era of potent antiretroviral therapy CHARTER study. Neurology 75: 2087–2096 10.1212/WNL.0b013e318200d727.
  10. 10. Simioni S, Cavassini M, Annoni J, Abraham AR, Bourquin I, et al. (2010) Cognitive dysfunction in HIV patients despite long-standing suppression of viremia. AIDS 24: 1243–1250 10.1097/QAD.0b013e3283354a7b.
  11. 11. Heaton RK, Franklin DR, Ellis RJ, McCutchan JA, Letendre SL, et al. (2011) HIV-associated neurocognitive disorders before and during the era of combination antiretroviral therapy: Differences in rates, nature, and predictors. J Neurovirol 17: 3–16 10.1007/s13365-010-0006-1.
  12. 12. Al-Khindi T, Zakzanis KK, van Gorp WG (2011) Does antiretroviral therapy improve HIV-associated cognitive impairment? A quantitative review of the literature. JINS 17: 956–969 10.1017/S1355617711000968.
  13. 13. Dawes S, Suarez P, Casey CY, Cherner M, Marcotte TD, et al. (2008) Variable patterns of neuropsychological performance in HIV-1 infection. J Clin Exp Neuropsychol 30: 613–626 10.1080/13803390701565225.
  14. 14. Hart RP, Wade JB, Klinger RL, Levenson JL (1990) Slowed information-processing as an early cognitive change associated with aids and arc. J Clin Exp Neuropsychol 12: 72–72.
  15. 15. Llorente AM, Miller EN, D'Elia LF, Selnes OA, Wesch J, et al. (1998) Slowed information processing in HIV-1 disease. J Clin Exp Neuropsychol 20: 60–72.
  16. 16. Carey CL, Woods SP, Rippeth JD, Gonzalez R, Moore DJ, et al. (2004) Initial validation of a screening battery for the detection of HIV-associated cognitive impairment. Clin Neuropsychol 18: 234–248.
  17. 17. Chang L, Ernst T, Witt MD, Ames N, Gaiefsky M, et al. (2002) Relationships among brain metabolites, cognitive function, and viral loads in antiretroviral-naive HIV patients. Neuroimage 17: 1638–1648 10.1006/nimg.2002.1254.
  18. 18. Judd F, Komiti A, Chua P, Mijch A, Hoy J, et al. (2005) Nature of depression in patients with HIV/AIDS. Aust N Z J Psychiatry 39: 826–832 10.1080/j.1440-1614.2005.01659.x.
  19. 19. Peavy G, Jacobs D, Salmon DP, Butters N, Delis DC, et al. (1994) Verbal memory performance of patients with human-immunodeficiency-virus infection - evidence of subcortical dysfunction. J Clin Exp Neuropsychol 16: 508–523 10.1080/01688639408402662.
  20. 20. Woods SP, Scott JC, Dawson MS, Morgan EE, Carey CL, et al. (2005) Construct validity of hopkins verbal learning test - revised component process measures in an HIV-1 sample. Arch Clin Neuropsychol 20: 1061–1071 10.1016/j.acn.2005.06.007.
  21. 21. Carey CL, Woods SL, Rippeth JD, Heaton RK, Grant I, et al. (2006) Prospective memory in HIV-1 infection. J Clin Exp Neuropsychol 28: 536–548 10.1080/13803390590949494.
  22. 22. Martin EM, Nixon H, Pitrak DL, Weddington W, Rains NA, et al. (2007) Characteristics of prospective memory deficits in HIV-seropositive substance-dependent individuals: Preliminary observations. J Clin Exp Neuropsychol 29: 496–504 10.1080/13803390600800970.
  23. 23. Maki PM, Cohen MH, Weber K, Little DM, Fornelli D, et al. (2009) Impairments in memory and hippocampal function in HIV-positive vs HIV-negative women. Neurology 72: 1661–1668 10.1212/WNL.0b013e3181a55f65.
  24. 24. Hinkin CH, Hardy DJ, Mason KI, Catellon SA, Lam MN, et al. (2002) Verbal and spatial working memory performance among HIV-infected adults. JINS 8: 532–538 10.1007/S1355617701020276.
  25. 25. Martin E, Pitrak D, Weddington W, Rains N, Nunnally G, et al. (2004) Cognitive impulsivity and HIV serostatus in substance dependent males. JINS 10: 931–938 10.1017/S1355617704107054.
  26. 26. Hardy D, Hinkin C, Levine A, Castellon S, Lam M (2006) Risky decision making assessed with the gambling task in adults with HIV. Neuropsychology 20: 355–360 10.1037/0894-4105.20.3.355.
  27. 27. Iudicello JE, Woods SP, Parsons TD, Moran LM, Carey CL, et al. (2007) Verbal fluency in HIV infection: A meta-analytic review. JINS 13: 183–189 10.1017/S1355617707070221.
  28. 28. Iudicello JE, Woods SP, Weber E, Dawson MS, Scott JC, et al. (2008) Cognitive mechanisms of switching in HIV-associated category fluency deficits. J Clin Exp Neuropsychol 30: 797–804 10.1080/13803390701779578.
  29. 29. Munoz-Moreno JA, Fumaz CR, Ferrer MJ, Prats A, Negredo E, et al. (2008) Nadir CD4 cell count predicts neurocognitive impairment in HIV-infected patients. AIDS Res Hum Retroviruses 24: 1301–1307 10.1089/aid.2007.0310.
  30. 30. Cattie JE, Doyle K, Weber E, Grant I, Woods SP, et al. (2012) Planning deficits in HIV-associated neurocognitive disorders: Component processes, cognitive correlates, and implications for everyday functioning. J Clin Exp Neuropsychol 34: 906–918 10.1080/13803395.2012.692772.
  31. 31. Stern RA, Arruda JE, Somerville JA, Cohen RA, Boland RJ, et al. (1998) Neurobehavioral functioning in asymptomatic HIV-1 infected women. JINS 4: 172–178.
  32. 32. Levine AJ, Hinkin CH, Miller EN, Becker JT, Selnes OA, et al. (2007) The generalizability of neurocognitive test/retest data derived from a nonclinical sample for detecting change among two HIV+ cohorts. J Clin Exp Neuropsychol 29: 669–678 10.1080/13803390600920471.
  33. 33. Manly JJ, Smith C, Crystal HA, Richardson J, Golub ET, et al. (2011) Relationship of ethnicity, age, education, and reading level to speed and executive function among HIV+ and HIV-women: The women's interagency HIV study (WIHS) neurocognitive substudy. J Clin Exp Neuropsychol 33: 853–863 10.1080/13803395.2010.547662.
  34. 34. Lopez O, Wess J, Sanchez J, Dew M, Becker J (1999) Neurological characteristics of HIV-infected men and women seeking primary medical care. Eur J Neurol 6: 205–209 10.1111/j.1468-1331.1999.tb00014.x.
  35. 35. Gatali M, Archibald C (2004) Women and HIV. BMC Womens Health 4 (Suppl 1) S27.
  36. 36. Maki PM, Martin-Thormeyer E (2009) HIV, cognition and women. Neuropsychol Rev 19: 204–214.
  37. 37. Logie CH, James L, Tharao W, Loutfy MR (2011) HIV, gender, race, sexual orientation, and sex work: A qualitative study of intersectional stigma experienced by HIV-positive women in Ontario, Canada. PLoS Med 8: e1001124 10.1371/journal.pmed.1001124.
  38. 38. Wang X, Salters KA, Zhang W, McCandless L, Money D, et al. (2012) Women's health care utilization among harder-to-reach HIV-infected women ever on antiretroviral therapy in British Columbia. AIDS Res Treat 560361
  39. 39. Carter AJ, Min JE, Chau W, Lima VD, Pick N, et al. (2013) Gender inequities in ‘quality of initial HIV care’ (QOC) among HIV-positive individuals initiating antiretroviral treatment in British Columbia, Canada (2000–2010). Can J Infect Dis Med Microbiol 24: p19A.
  40. 40. Ellis RJ, Badiee J, Vaida F, Letendre S, Heaton RK, et al. (2011) CD4 nadir is a predictor of HIV neurocognitive impairment in the era of combination antiretroviral therapy. AIDS 25: 1747–1751 10.1097/QAD.0b013e32834a40cd.
  41. 41. Tozzi V, Balestra P, Galgani S, Narciso P, Ferri F, et al. (1999) Positive and sustained effects of highly active antiretroviral therapy on HIV-1-associated neurocognitive impairment. AIDS 13: 1889–1897 10.1097/00002030-199910010-00011.
  42. 42. Devlin KN, Gongvatana A, Clark US, Chasman JD, Westbrook ML, et al. (2012) Neurocognitive effects of HIV, hepatitis C, and substance use history. JINS 18: 68–78 10.1017/S1355617711001408.
  43. 43. Gonzalez R, Heaton RK, Moore DJ, Letendre S, Ellis RJ, et al. (2003) Computerized reaction time battery versus a traditional neuropsychological battery: Detecting HIV-related impairments. JINS 9: 64–71 10.1017/S1355617703910071.
  44. 44. Paul R, Flanigan TP, Tashima K, Cohen R, Lawrence J, et al. (2005) Apathy correlates with cognitive function but not CD4 status in patients with human immunodeficiency virus. J Neuropsychiatry Clin Neurosci 17: 114–118 10.1176/appi.neuropsych.17.1.114.
  45. 45. Vitiello B, Goodkin K, Ashtana D, Shapshak P, Atkinson JH, et al. (2007) HIV-1 RNA concentration and cognitive performance in a cohort of HIV-positive people. AIDS 21: 1415–1422 10.1097/QAD.0b013e328220e71a.
  46. 46. Ettenhofer ML, Hinkin CH, Castellon SA, Durvasula R, Ullman J, et al. (2009) Aging, neurocognition, and medication adherence in HIV infection. Am J Geriatr Psychiatry 17: 281–290.
  47. 47. Shimizu SM, Chow DC, Valcour V, Masaki K, Nakamoto B, et al. (2011) The impact of depressive symptoms on neuropsychological performance tests in HIV-infected individuals: A study of the hawaii aging with HIV cohort. World J AIDS 1: 139–145.
  48. 48. Clifford DB, Evans SR, Yang YJ, Gulick RM (2005) The neuropsychological and neurological impact of hepatitis C virus co-infection in HIV-infected subjects. AIDS 19: S64–S71 10.1097/01.aids.0000192072.80572.43.
  49. 49. Richardson JL, Nowicki M, Danley K, Martin EM, Cohen MH, et al. (2005) Neuropsychological functioning in a cohort of HIV- and hepatitis C virus-infected women. AIDS 19: 1659–1667.
  50. 50. Hinkin CH, Castellon SA, Levine AJ, Barclay TR, Singer EJ (2008) Neurocognition in individuals co-infected with HIV and hepatitis C. J Addict Dis 27: 11–17 10.1300/J069v27n02_02.
  51. 51. Durvasula RS, Miller EN, Myers HF, Wyatt GE (2001) Predictors of neuropsychological performance in HIV positive women. J Clin Exp Neuropsychol 23: 149–163.
  52. 52. Margolin A, Avants SK, Warburton LA, Hawkins KA (2002) Factors affecting cognitive functioning in a sample of human immunodeficiency virus-positive injection drug users. AIDS Patient Care STDS 16: 255–267.
  53. 53. Durvasula RS, Myers HF, Mason K, Hinkin C (2006) Relationship between alcohol use/abuse, HIV infection and neuropsychological performance in African American men. J Clin Exp Neuropsychol 28: 383–404 10.1080/13803390590935408.
  54. 54. Guaraldi G, Orlando G, Zona S, Menozzi M, Carli F, et al. (2011) Premature age-related comorbidities among HIV-infected persons compared with the general population. Clin Infect Dis 53: 1120–1126 10.1093/cid/cir627.
  55. 55. Pathai S, Lawn SD, Gilbert CE, McGuinness D, McGlynn L, et al. (2013) Accelerated biological ageing in HIV-infected individuals in South Africa: A case-control study. AIDS 27: 2375–2384.
  56. 56. Malan-Mueller S, Hemmings SMJ, Spies G, Kidd M, Fennema-Notestine C, et al. (2013) Shorter telomere length - A potential susceptibility factor for HIV-associated neurocognitive impairments in South African woman. PLoS One 8: e58351 10.1371/journal.pone.0058351.
  57. 57. Zanet D L, Sattha B, Maan E, Thorne A, Gadawski I, et al. (2012) Factors associated with shorter leukocyte telomere length in HIV+ and HIV- adults. Can J Infect Dis Med Microbiol 23 (Suppl A) Oral Presentation 0008.
  58. 58. Effros R, Allsopp R, Chiu C, Hausner M, Hirji K, et al. (1996) Shortened telomeres in the expanded CD28− CD8+ cell subset in HIV disease implicate replicative senescence in HIV pathogenesis. AIDS 10: F17–F22 10.1097/00002030-199607000-00001.
  59. 59. Palmer L, Weng N, Levine B, June C, Lane H, et al. (1997) Telomere length, telomerase activity, and replicative potential in HIV infection: Analysis of CD4(+) and CD8(+) T cells from HIV-discordant monozygotic twins. J Exp Med 185: 1381–1386 10.1084/jem.185.7.1381.
  60. 60. Pommier J, Gauthier L, Livartowski J, Galanaud P, Boue F, et al. (1997) Immunosenescence in HIV pathogenesis. Virology 231: 148–154 10.1006/viro.1997.8512.
  61. 61. Rickabaugh TM, Kilpatrick RD, Hultin LE, Hultin PM, Hausner MA, et al. (2011) The dual impact of HIV-1 infection and aging on naive CD4(+) T-cells: Additive and distinct patterns of impairment. PLoS One 6: e16459 10.1371/journal.pone.0016459.
  62. 62. Hearps AC, Maisa A, Cheng W, Angelovich TA, Lichtfuss GF, et al. (2012) HIV infection induces age-related changes to monocytes and innate immune activation in young men that persist despite combination antiretroviral therapy. AIDS 26: 843–853 10.1097/QAD.0b013e328351f756.
  63. 63. Cote HCF, Soudeyns H, Thorne A, Alimenti A, Lamarre V, et al. (2012) Leukocyte telomere length in HIV-infected and HIV-exposed uninfected children: Shorter telomeres with uncontrolled HIV viremia. PLoS One 7: e39266 10.1371/journal.pone.0039266.
  64. 64. von Zglinicki T, Serra V, Lorenz M, Saretzki G, Lenzen-Grossimlighaus R, et al. (2000) Short telomeres in patients with vascular dementia: An indicator of low antioxidative capacity and a possible risk factor? Lab Invest 80: 1739–1747 10.1038/labinvest.3780184.
  65. 65. Panossian LA, Porter VR, Valenzuela HF, Zhu X, Reback E, et al. (2003) Telomere shortening in T cells correlates with Alzheimer's disease status. Neurobiol Aging 24: 77–84 10.1016/S0197-4580(02)00043-X.
  66. 66. Honig LS, Schupf N, Lee JH, Tang MX, Mayeux R (2006) Shorter telomeres are associated with mortality in those with APOE epsilon 4 and dementia. Ann Neurol 60: 181–187 10.1002/ana.20894.
  67. 67. Martin-Ruiz C, Dickinson HO, Keys B, Rowan E, Kenny RA, et al. (2006) Telomere length predicts poststroke mortality, dementia, and cognitive decline. Ann Neurol 60: 174–180 10.1002/ana.20869.
  68. 68. Valdes AM, Deary IJ, Gardner JP, Kimura M, Lu X, et al. (2008) Leukocyte telomere length is associated with cognitive performance in healthy women. Neurobiol Aging 1–7.
  69. 69. Yaffe K, Lindquist K, Kluse M, Cawthon R, Harris T, et al. (2011) Telomere length and cognitive function in community-dwelling elders: Findings from the health ABC study. Neurobiol Aging 32: 2055–2060 10.1016/j.neurobiolaging.2009.12.006.
  70. 70. Antinori A, Arendt G, Becker JT, Brew BJ, Byrd DA, et al. (2007) Updated research nosology for HIV-associated neurocognitive disorders. Neurology 69: 1789–1799 10.1212/01.WNL.0000287431.88658.8b.
  71. 71. Burgess P, Alderman N, Evans J, Emslie H, Wilson B (1998) The ecological validity of tests of executive function. JINS 4: 547–558.
  72. 72. Bell-McGinty S, Podell K, Franzen M, Baird A, Williams M (2002) Standard measures of executive function in predicting instrumental activities of daily living in older adults. Int J Geriatr Psychiatry 17: 828–834 10.1002/gps.646.
  73. 73. Cahn-Weiner D, Boyle PA, Malloy PF (2002) Tests of executive function predict instrumental activities of daily living in community-dwelling older individuals. Appl Neuropsychol 9: 187–191 10.1207/S15324826AN0903_8.
  74. 74. Zanet DL, Saberi S, Oliveira L, Sattha B, Gadawski I, et al. (2013) Blood and dried blood spot telomere length measurement by qPCR: Assay considerations. PLoS One 8: e57787 10.1371/journal.pone.0057787.
  75. 75. Radloff LS (1977) The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, Appl Psychol Meas 1: 385–401.
  76. 76. The Psychological Corporation (2001) Wechsler test of adult reading. San Antonio, TX: Harcourt Assessment.
  77. 77. Fray P, Robbins T, Sahakian B (1996) Neuropsychiatric applications of CANTAB. Int J Geriatr Psychiatry 11: 329–336.
  78. 78. Ruff R, Parker S (1993) Gender-specific and age-specific changes in motor speed and eye-hand coordination in adults - normative values for the finger tapping and grooved pegboard tests. Percept Mot Skills 76: 1219–1230.
  79. 79. Brandt J, Benedict RHB (2001) Hopkins verbal learning test - revised: Professional manual. Lutz, FL: Psychological Assessment Resources Inc.
  80. 80. Wechsler D (2008) WAIS-IV administration and scoring manual (Canadian). San Antonio, TX: The Psychological Corporation.
  81. 81. Robbins TW, James M, Owen AM, Sahakian BJ, Lawrence AD, et al. (1998) A study of performance on tests from the CANTAB battery sensitive to frontal lobe dysfunction in a large sample of normal volunteers: Implications for theories of executive functioning and cognitive aging. JINS 4: 474–490.
  82. 82. Miyake A, Friedman NP, Emerson MJ, Witzki AH, Howerter A, et al. (2000) The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cogn Psychol 41: 49–100 10.1006/cogp.1999.0734.
  83. 83. Testa R, Bennett P, Ponsford J (2012) Factor analysis of nineteen executive function tests in a healthy adult population. Arch Clin Neuropsychol 27: 213–224 10.1093/arclin/acr112.
  84. 84. Richardson JL, Martin EM, Jimenez N, Danley K, Cohen M, et al. (2002) Neuropsychological functioning in a cohort of HIV infected women: Importance of antiretroviral therapy. JINS 8: 781–793.
  85. 85. Hestad KA, Menon JA, Silalukey-Ngoma M, Franklin DR Jr, Imasiku ML, et al. (2012) Sex differences in neuropsychological performance as an effect of human immunodeficiency virus infection A pilot study in Zambia, Africa. J Nerv Ment Dis 200: 336–342 10.1097/NMD.0b013e31824cc225.
  86. 86. Cohen J (1992) A power primer. Psychol Bull 112: 155–159 10.1037/0033-2909.112.1.155.
  87. 87. Hedges L, Olkin I (1984) Nonparametric estimators of effect size in meta-analysis. Psychol Bull 96: 573–580 10.1037//0033-2909.96.3.573.
  88. 88. Cohen J, Cohen P, West SG, Aiken LS (2003) Categorical or nominal independent variables. In: D. Riegert, editor. Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates, Inc. pp. 302–353.
  89. 89. Benowitz N (1993) Clinical-pharmacology and toxicology of cocaine. Pharmacol Toxicol 72: 3–12.
  90. 90. Meyer JS, Quenzer LF (2005) Psychomotor stimulants: Cocaine and the amphetamines. In: JS Meyer, LF Quenzer, editors. Psychopharmacology: Drugs, the brain, and behavior. Sunderland, MA: Sinauer Associates, Inc. pp. 276–300.
  91. 91. Spronk DB, van Wel JHP, Ramaekers JG, Verkes RJ (2013) Characterizing the cognitive effects of cocaine: A comprehensive review. Neurosci Biobehav Rev 37: 1838–1859.
  92. 92. Potter GG, McQuoid DR, Payne ME, Taylor WD, Steffens DC (2012) Association of attentional shift and reversal learning to functional deficits in geriatric depression. Int J Geriatr Psychiatry 27: 1172–1179 10.1002/gps.3764.
  93. 93. Tabachnick B, Fidell L (2007) Using multivariate statistics. Boston, MA: Pearson. 980 p.
  94. 94. Gibbie T, Mijch A, Ellen S, Hoy J, Hutchison C, et al. (2006) Depression and neurocognitive performance in individuals with HIV/AIDS: 2-year follow-up. HIV Med 7: 112–121 10.1111/j.1468-1293.2006.00350.x.
  95. 95. Fray PJ, Robbins TW (1996) CANTAB battery: Proposed utility in neurotoxicology. Neurotoxicol Teratol 18: 499–504.
  96. 96. Stuss D, Levine B (2002) Adult clinical neuropsychology: Lessons from studies of the frontal lobes. Annu Rev Psychol 53: 401–433 10.1146/annurev.psych.53.100901.135220.
  97. 97. Fellows L, Farah M (2003) Ventromedial frontal cortex mediates affective shifting in humans: Evidence from a reversal learning paradigm. Brain 126: 1830–1837 10.1093/brain/awg180.
  98. 98. Hornak J, O'Doherty J, Bramham J, Rolls E, Morris R, et al. (2004) Reward-related reversal learning after surgical excisions in orbito-frontal or dorsolateral prefrontal cortex in humans. J Cogn Neurosci 16: 463–478 10.1162/089892904322926791.
  99. 99. Hampshire A, Owen AM (2006) Fractionating attentional control using event-related fMRI. Cereb Cortex 16: 1679–1689 10.1093/cercor/bhj116.
  100. 100. Van der Molen MJW, Van der Molen MW, Ridderinkhof KR, Hamel BCJ, Curfs LMG, et al. (2012) Attentional set-shifting in fragile X syndrome. Brain Cogn 78: 206–217 10.1016/j.bandc.2011.12.008.
  101. 101. Leeson VC, Robbins TW, Matheson E, Hutton SB, Ron MA, et al. (2009) Discrimination learning, reversal, and set-shifting in first-episode schizophrenia: Stability over six years and specific associations with medication type and disorganization syndrome. Biol Psychiatry 66: 586–593 10.1016/j.biopsych.2009.05.016.
  102. 102. Faílde-Garrido JM, Rodríguez Alvarez M, Simón-López MA (2008) Neuropsychological impairment and gender differences in HIV-1 infection. Psychiatry Clin Neurosci 62: 494–502 10.1111/j.1440-1819.2008.01841.x.
  103. 103. Mather KA, Jorm AF, Parslow RA, Christensen H (2011) Is telomere length a biomarker of aging? A review. J Gerontol A Biol Sci Med Sci 66: 202–213 10.1093/gerona/glq180.
  104. 104. Cherkas LF, Hunkin JL, Kato BS, Richards JB, Gardner JP, et al. (2008) The association between physical activity in leisure time and leukocyte telomere length. Arch Intern Med 168: 154–158 10.1001/archinternmed.2007.39.
  105. 105. Kyo S, Takakura M, Kanaya T, Zhuo W, Fujimoto K, et al. (1999) Estrogen activates telomerase. Cancer Res 59: 5917–5921.
  106. 106. Letendre S, Marquie-Beck J, Capparelli E, Best B, Clifford D, et al. (2008) Validation of the CNS penetration-effectiveness rank for quantifying antiretroviral penetration into the central nervous system. Arch Neurol 65: 65–70 10.1001/archneurol.2007.31.
  107. 107. Clark L, Cools R, Robbins T (2004) The neuropsychology of ventral prefrontal cortex: Decision-making and reversal learning. Brain Cogn 55: 41–53 10.1016/s0278-2626(03)00284-7.
  108. 108. Owsley C, Sloane M, McGwin G, Ball K (2002) Timed instrumental activities of daily living tasks: Relationship to cognitive function and everyday performance assessments in older adults. Gerontology 48: 254–265 10.1159/000058360.
  109. 109. Vigil O, Posada C, Woods SP, Atkinson H, Heaton RK, et al. (2008) Impairments in fine-motor coordination and speed of information processing predict declines in everyday functioning in hepatitis C infection. J Clin Exp Neuropsychol 30: 805–815 10.1080/13803390701802354.
  110. 110. Ettenhofer ML, Foley J, Castellon SA, Hinkin CH (2010) Reciprocal prediction of medication adherence and neurocognition in HIV/AIDS. Neurology 74: 1217–1222.