This manuscript investigated the relationship between nadir CD4 cell count, CD4 and PVL counts 1 year prior to cognitive testing, the change in CD4 and PVL during the year prior to cognitive testing, and current cognitive performance. We used a multivariate modeling approach to determine whether these clinical variables would be useful in predicting current cognitive performance. Nadir CD4 cell counts have been used successfully to predict global cognitive function and rates of dementia illustrating a relationship between a patient's clinical history and CNS injury. Results from the current study suggest that even in the era of cART lower nadir CD4 cell counts are predictive of worse executive function. Language, attention, learning and memory, and processing speed were not found to be significantly related to nadir CD4 cell counts. To our knowledge, this is the first attempt to examine the association between nadir CD4 cell counts and specific cognitive domains. The specificity of this finding for executive function suggests that there may be special significance of the severity of immunosuppression and executive function.
We also examined 1-year clinical history as a predictor of current cognitive function. This allows incorporation of within-subject variability of clinical measures (CD4 cell counts and PVL) which is not possible in traditional cross-sectional designs. We found that CD4 cell count is associated with performance in two cognitive domains, namely executive function and attention. The trend in CD4 cell counts (change over the previous year) was not found to be significantly associated with any cognitive domains after controlling for predicted CD4 cell count. In addition, compared with those whose PVL status stayed the same or worsened, patients whose PVL status improved demonstrated better cognitive performance for executive function and learning/memory domains.
For these cognitive domains, this suggests, in general, that recent clinical history is more important than nadir CD4 cell counts. In addition, the addition of nadir CD4 to these models did not improve the fit to scores in the attention, language/fluency, speed of processing, or motor function domains. Although nadir CD4 cell counts and the predicted CD4 cell count were significantly associated with executive function when examined separately, the significance and effect sizes were attenuated and became insignificant when both variables were included in the same model. As a result, we could not compare the joint effects of nadir CD4 and CD4 count 1 year prior to testing on executive function due to multicollinearity. Regardless, these findings emphasize the need to examine common measures of immune function for trends that might provide additional insight into the development and progression of cognitive dysfunction in HIV-infected patient cohorts.
Further, elucidation of these relationships in other HIV-infected patient cohorts may prove to be particularly important for several reasons. First, these findings provide additional insight into the evolution of cognitive dysfunction where a recent history of improving immunological function heralds improved cognitive performance, especially for attention, executive function, and learning/memory. This is similar to the results of the Bornstein and colleagues (1991)
pre-cART era study that described improving cognitive benefits (reaction time and memory) in HIV-infected patients who experienced better improvement in CD4 cell count percentage change. It is clear that additional studies will be needed to understand the exact nature of these temporal relationships, as the current study does not include any prospective cognitive data. This fact precludes us from making definitive statements regarding cause and effect, but it does illustrate the value of examining such relationships. Second, as we know, deficits in these domains can negatively affect participation in activities of daily living, including medication management (Hinkin et al., 2004
). Understanding these relationships will be increasingly important as we continue to manage the long-term chronic aspects of this disease. Third, further elucidation of these temporal relationships will not only provide us with relevant biomarkers of CNS injury, but it will also elucidate the necessary changes or trajectories in these markers that are sufficient to cause CNS injury or repair. In many ways, these findings also emphasize the need to broaden our search for additional biomarkers that may better explain CNS injury and/or provide clinical markers for evaluating treatment efficacy. For example, there have been studies examining other biomarkers of immune activation and neurologic complications like monocyte chemoattractant protein, tumor necrosis factor, and cell surface markers. These studies have found significant associations between these markers of immune activation and neurologic complications (Kusdra, McGuire, & Pulliam, 2002
; McArthur, Brew, & Nath, 2005
) though it is not clear what effect they may have on cognitive performance. Though modeling recent CD4 and PVL history is significantly predictive of attention/working memory and executive dysfunction in this study, studies of additional biomarkers are required.
These findings should be interpreted cautiously for several reasons. First, this is a retrospective archival study. It could be argued that the data used were not designed to specifically address the research questions in this study. Certainly, there are several potentially confounding factors not directly addressed given the archival nature of the disease severity measures including uncontrolled demographic features like potential changes in drug/alcohol abuse histories. It should be noted that a significant number of our sample met the addiction criteria for drug/alcohol (>50%; Table ) sometime in their past as assessed by the KMSK. It is clear from the literature that drug/alcohol use in HIV-infected patients is a potential confound and changes in their use histories during the course of the study could affect their neuropsychological performance as well. However, inclusion criteria for both studies required that each participant to not be using alcohol or drugs 6 months prior to enrollment that afforded some control of this potential confound. Second, the normative data that were used to calculate standard scores were based on an international database of healthy subjects. The use of this specific normative data may be considered a weakness, as there may be important demographic features that vary from that of the HIV-infected patients. Importantly, however, the data did allow us to produce standardized scores that were corrected for age, education, and gender.
Regardless, these findings extend the current literature in several ways. First, to our knowledge, this is the first paper to examine the relationship between recent history of CD4 and PVL and cognitive performance using random effects statistical modeling in the cART era. As already discussed, in the pre-cART era, Bornstein and colleagues (1991)
demonstrated a significant relationship between rates of CD4 change (using two time points) and measures of reaction time and memory. Though their method (calculating the percent change between two time points) is a simpler approach to this question, it is unable to account for individual variance in each patient's disease severity measures. Additionally, random effects modeling has proven useful in examining several clinical outcomes for progressive neurological conditions, such as MS and Alzheimer's disease, and if validated further in larger prospective HIV-infected cohorts could reasonably result in the development of mathematical models useful in predicting cognitive dysfunction. Second, use of recent history of key clinical variables may have additional utility over the use of other historical markers of disease severity such as nadir CD4. Though nadir CD4 cell count has been used with a significant amount of success when predicting cognitive outcome (Robertson et al., 2004
; Tozzi et al., 2005
; Valcour et al., 2006
), there are still many questions left unanswered by using a single historical time point. We know that clinical variables may fluctuate and we also know that a certain percentage of patients experience a pattern of fluctuating cognitive performance over the course of the disease. Thus, examining the relationship between measures from a single time point may or may not yield any significant findings especially if there are temporal lags in the relationship between variables. Together, these observations require new approaches to examining the variability of these data to answer important questions regarding clinical/cognitive relationships. Third, we examine the effects of these variables on specific tests of cognitive function rather than global measures of cognitive function or rates of dementia. By using a broader based battery of cognitive tests, we were able to assess multiple domains. This may be increasingly important in the era of cART and in the context of better viral suppression where the cognitive profile (or phenotype) may be evolving (Brew, 2004
In summary, these findings suggest that the patient's recent clinical history is an important predictor of current attention/working memory and executive dysfunction. If validated, modeling of recent clinical variables may dramatically improve our understanding of the evolution and progression of HIV-associated CNS injury providing researchers and clinicians with the much needed objective markers of CNS involvement among HIV-infected cohorts.