Although combined antiretroviral therapy (cART) has become a highly effective tool in controlling replication of the HIV, cART is not completely effective in suppressing the neurological complications of HIV infection [1–6]. Although some cases of cognitive impairment can be attributed to cART failure, many individuals develop HIV-associated neurocognitive disorders (HAND) with stable cART, low levels or undetectable viral load, and CD4+ cell counts in the normal range . Understanding the aetiologic basis of HAND in this population is critically important to the development of adjunctive therapies to protect and restore the brain in HIV-infected individuals.
Several lines of evidence suggest that HIV infection and cART are each associated with disturbances in peripheral and central bioenergetics disturbances [8–14]. Whereas brain imaging emphasizes the importance of metabolic abnormalities in HIV-infected patients, the underlying bioenergetic pathways modified by infection are not understood. In this study, we identify specific metabolite changes in cerebrospinal fluid (CSF) of HIV-infected patients, and use partial least squares discriminant analysis (PLS-DA) modelling with nonparametric statistical approaches to determine the relationship of these changes to the longitudinal trajectories of neurocognitive function in HIV-infected patients.
Materials and methods
Patients and sample collection
Ninety-nine CSF samples from the Central Nervous System HIV Anti-Retroviral Therapy Effects Research study were selected based on a case review of approximately 3500 clinical visits from 430 study participants as recently described . Patients met the following criteria: three consecutive visits with complete clinical and neuropsychological data, stable cART for at least 3 months before the first visit and throughout the duration of this study period (a single drug modification in cART was allowed if within the same drug class). Based on temporal change in neurocognitive functioning, patients were grouped as normal (n = 25), improving (n = 23), worsening (n = 25) and stably impaired (n = 26). See  for a detailed description of criteria in neurocognitive status. Group demographics were similar, as were HIV duration, hepatitis C virus status, nadir CD4+, plasma and CSF viral load. There were significant group differences in current CD4+ and CSF storage time (SI1). Metabolite analysis was conducted using CSF from the first two visits, and neurocognitive data from the third visit were used to validate the trajectory of change in cognitive functioning.
Neurocognitive test battery
The following seven cognitive domains were evaluated: executive function [16,17], learning and delayed recall [18,19], working memory [20,21], verbal fluency , speed of information processing [17,20,21] and motor skills . Deficit scores were calculated as previously described .
Nuclear magnetic resonance spectroscopy
CSF samples (50 μl each) were placed in a 5 mm nuclear magnetic resonance (NMR) tube and diluted to a final volume of 600 μl with phosphate buffer (0.2 mol/l Na2HPO4/0.04 NaH2PO4, pH 7.4, 0.1% sodium azide, 0.8% sodium chloride) in D2O containing 1 mmol/l TSP (3-trimethylsilyl-1-[2,2,3,3,-2H4] propionate) as an internal standard. 1H NMR spectra were acquired from each sample using a 16.4 T NMR (700 MHz 1H) system (Bruker Avance III equipped with a 1H TCI cryoprobe; Bruker Biospin, Germany). For all samples a one-dimensional nuclear Overhauser effect spectroscopy (NOESY) presaturation sequence, with solvent presaturation during the relaxation delay (2 s) and mixing time (10 ms), was used with 32 scans. All one-dimensional spectra were automatically baseline corrected using a 0th order polynomial (Topspin 3.0; Bruker Biospin, Germany). Two dimensional 1H nuclear magnetic resonance (1H NMR) spectra were acquired from a sample within each group to assist with metabolite identification. The two-dimensional correlation spectroscopy (COSY) spectra were acquired on the same spectrometer as the one-dimensional NMR spectra. The COSY spectra were acquired with 1.5 s solvent presaturation, a spectral width of 10 ppm (7002 Hz), and 32 transients per t1 increment for 256 increments. All NMR experiments were acquired at 293K.
The one-dimensional 1H spectra were subdivided into 0.02 ppm regions (δ = midpoint of integral region) and integrated using ACD/SpecManager version 12 (Advanced Chemistry Development, UK). Thus, spectra were reduced to 435 independent variables between 0.20–4.30 and 5.00–9.60 ppm. The region between 4.30 and 5.00 ppm was omitted because of spectrum distortion arising from the water suppression at 4.7 ppm. The buckets containing only baseline noise were removed from the analysis. Using these criteria, 66 buckets were identified that contained 1H NMR resonances. These buckets were 1.32-1.34, 1.46-1.48, 1.90-1.92, 2.10-2.16, 2.36-2.70, 3.02-3.06, 3.22-3.64, 3.68-4.14, 5.22-5.24 (including all 0.02 ppm buckets between the two values). Baseline noise was removed by visual inspection of the spectra (SI2 A). The spectra were overlaid (Topspin 3.0, Bruker), and the regions containing peaks visible over the background noise were selected. All data were scaled using the Pareto variance to suppress noise. Subsequently, statistical pattern recognition was applied to the spectra to differentiate between CSF samples obtained from patients with different cognitive states. The metabolites were identified using a combination of two-dimensional COSY spectroscopy (SI2 B) and reference to the Human Metabolome database [23–25].
Linear regression analysis
Associations between each metabolite and domain-specific cognitive performance scores at baseline were determined using linear regression controlling for age, education, nadir CD4+ and storage time. In these analyses, we included all individuals regardless of longitudinal group assignment. If the metabolite signal appeared in multiple bins within the spectra, the bin with the largest integral value was selected for analysis. The a priori P value was set at P < 0.05. All tests were two sided. These analyses were performed using Stata 12 (StataCorp: Stata Statistical Software, College Station, Texas, USA).
For group comparisons, PLS-DA and orthogonal partial least squares discriminant analysis (OPLS-DA) models were derived that best explained the differences between the four cognitive groups (SIMCA P+ 12.0; Umetrics, Sweden). We determined the predictive value of the models using the q2 value derived from a stepwise cross-validation of the model. In this validation, a fraction of the samples are removed, the model is rebuilt and the new model is used to predict the class of the removed samples. Specifically, q2 represents the predicted residual sum of squares divided by the initial sum of squares and subtracted from 1. A value of q2 more than 0 means that the model is predictive and a value of more than 0.4 is typically used as the threshold for significance in biological modelling . Model validation was also carried out within SIMCA using a pseudo-Monte Carlo method in which we built 100 models with the samples assigned to random groups. The goodness-of-fit of each of these randomly permuted models was compared with the fit for the models produced using experimentally defined groups. Only models in which the genuine q2 value was higher than 95% of the randomly generated q2 values were considered predictive.
Risk estimation analysis
Decision models were developed by recursive partitioning . Metabolites and clinical variables measured at the first visit and, in separate analyses, baseline metabolites combined with change between visits for clinical variables were used as potential classifying criteria to assign groups based on temporal change in cognitive status. Five-fold cross-validation guarded against overfitting models to the sample. Initially, a full model was generated to achieve the best fit. Proceeding through paths of this decision model, if a variable previously entered into the model was followed by subsequent inclusion of the same variable with a different decision path, the subsequent variable was removed, and splitting of the tree was continued until no further additions improved the model. The resultant tree was then ‘pruned’ to reduce complexity until no further reduction was made without reaching a higher misclassification rate than the prespecified limit of 10% if possible. The pruning ensured that as few metabolites and clinical variables as possible would be needed to achieve the prespecified levels of accuracy. Confusion matrices were generated to tabulate the number of patients correctly and incorrectly classified to each group.
Cross-sectional associations between energy metabolites and domain-specific cognitive performance at baseline
1H NMR spectroscopy of CSF identified 14 energy metabolites in CSF (SI2 A-B and SI3 for full assignments). At baseline, increased CSF levels of pyroglutamate, citric acid, creatine and alanine were significantly associated with deficits in global cognitive function. All other energy metabolites identified showed similar trends, with the exception of glutamine (Table 1). To identify components of the global deficit score responsible for these associations, we grouped metabolites according to domain-specific cognitive scores. Deficits in executive functioning were associated with higher CSF concentrations of lactate, pyroglutamate, citrate and creatine. Verbal learning was negatively associated with higher pyroglutamate. Impairments in delayed recall were associated with higher pyroglutamate, creatine myo-inositol, alanine and acetate. Impairments in motor function were associated with higher concentrations of lactate, citrate, creatinine, myo-inositol, alanine and acetate. Verbal memory, speed of information processing and working memory were not associated with any energy metabolites (Table 1). As negative associations of these energy metabolites with cognitive function could represent a compensatory response, or may be causal of impairments in cognitive processing, we next sought to determine how energetic demand varied in association with changes in cognitive status.
Longitudinal multivariate modelling
Initial PLS-DA models did not reveal cross-sectional separations between improving, worsening, normal or impaired groups at baseline. However, visual separations were observed in the PLS-DA models between baseline and follow-up samples for each neurocognitive group (Fig. 1a). Each of these models was predictive with q2 values equal to 0.61 (normal), 0.84 (stably impaired), 0.47 (worsening) and 0.52 (improving). Identification of the specific metabolites that drove group differences in this first model was not possible because of interference from unidentified nondisease-related factors. We generated OPLS-DA models to remove these factors from the models.
Examination of the S plots generated by OPLS-DA modelling identified several metabolites that drove longitudinal separations between neurocognitive groups. The results from these unsupervised analyses identified several tricarboxylic acid (TCA) cycle energy metabolites and essential amino acids that were differentially regulated by neurocognitive status. We separated these into metabolites decreased (Fig. 1b), and increased at baseline relative to follow-up (Fig. 1c). Normal and worsening groups both had decreased levels of citric acid and pyroglutamic acid and increased glutamine and lactic acid at follow-up compared with baseline. Stably impaired and improved groups both showed decreased glutamine and glucose with increased citric acid, pyroglutamic acid and lactic acid at follow-up compared with baseline. CSF levels of myo-inositol increased in the stably impaired group. To better define these initial separations, we next used recursive partitioning and incorporated clinical variables to identify the group of variables that could potentially be used as prognostic indicators for changes in cognitive status.
Prognostic indicators for worsening and improving neurocognitive status
A prognostic identification for worsening contained seven features that included four clinical measures (nadir CD4+<322, current CD4+<136, detectable plasma viral load and off antiretrovirals) in addition to three metabolites (elevated glutamine and creatinine; decreased citrate) (Fig. 2a), to achieve an error rate of 14% (additional modifications in modelling did not lower the error rate) with a sensitivity of 96%, and specificity of 76% (Fig. 2a insert). Prognosis for improvement consisted of seven features that included three clinical factors (current CD4+<176, detectable plasma viral load, and hepatitis C virus negative), combined with four metabolites (elevated lactate and glutamine decreased glucose and acetate) (Fig. 2b) to produce a less than 10% error rate with 87% sensitivity and 96% specificity (Fig. 2b insert). As current disease status could confound these baseline prognostic indicators (i.e. by definition, those who improved most over time were the most impaired at baseline), we next analyzed change in individual clinical measures from baseline to follow-up.
Incorporating the trajectory of change in defined clinical measures with baseline metabolic features reduced model complexity
Incorporating change of clinical status into recursive partitioning reduced the complexity of the decision trees. Prognostic identification for worsening was achieved with four features that included one clinical measure (no change in a detectable plasma viral load), and three metabolites (elevated citrate and acetate; decreased creatine) (Fig. 3a), to produce a model with a predictive accuracy of 92%, 88% sensitivity and 96% specificity (Fig. 3a insert). Prognosis for improvement contained seven features that included two clinical measures (first visit age less than 47 years, new or continued use of antiretrovirals) (Fig. 3b), and five metabolites (elevated glutamine and glucose; decreased myo-inositol, β-glucose and creatinine) to generate a model with a predictive accuracy of 92%, a sensitivity of 100% and specificity of 84% (Fig. 3b insert).
This study demonstrates that CSF energy metabolites in HIV-infected patients are related to temporal changes in neurocognitive state. Overall, markers of increased aerobic metabolism were associated with worsening cognitive function, and markers of increased anaerobic metabolism tended to be associated with improving cognitive status in HIV-infected patients. Throughout the discussion, the reader is encouraged to refer to the decision trees for clarity, as each of the risk factors described depends on the value of other risk factors for inclusion into a particular group.
Worsening cognitive status
The brain is especially sensitive to alterations in energy substrate availability . In HIV-infected patients with declining neurocognitive status, we found evidence for increased glycolysis (Fig. 4). This is likely related to altered glial cell function as the conversion of acetate to acetyl-CoA occurs almost exclusively within the glial cells in the central nervous system [29,30], and carbon labelling of acetate in human studies has shown this anion to be readily converted to glutamate . Citrate accumulates when the glycolytic rate exceeds TCA cycle activity, and has been noted a key metabolic feature of some astrocytomas . When the citric acid cycle is saturated, citrate concentrations increase and directly inhibit phosphofructokinase, a rate-limiting enzyme of glycolysis. Among possible explanations, the two most likely are that there is an excessive glycolytic rate in patients with cognitive worsening or that there are deficiencies in the TCA cycle that slow citrate metabolism. However, the decrease of CSF creatine in patients with worsening cognitive status suggests that high brain energy demand may drive an increased glycolytic rate in patients with worsening cognitive status. Creatine is tightly linked to oxidative metabolism and a critical source of phosphate when energy demand is high . Energy production from phosphocreatine is regulated by the creatine phosphokinase system which catalyses a reversible reaction that uses adenosine triphosphate (ATP) to create phosphocreatine or liberates a high-energy phosphate from phosphocreatine to create ATP . One function of this energy source is to maintain ATP at a steady state by buffering excess ATP, and rapidly producing ATP as required. Elevated creatine and citrate in the CSF of HIV-infected patients with worsening cognitive function provides additional evidence that suggests an increased rate of glycolysis in these patients. Although these data do not provide the means to identify the underlying pathology driving this increase in energy demand, HIV is known to productively infect microglia [34–36], with a more restrictive infection of astrocytes [37–46]. Thus, we speculate that our CSF metabolic results may reflect the glial response to infection. However, we cannot rule out possible contributions of increased neuronal glycolysis which may directly contribute to the production of pyruvate for TCA metabolism .
Incorporating clinical findings and energy metabolites into recursive partitioning models revealed important contributions of nadir CD4+, current CD4+, plasma viral load and antiretroviral status to worsening cognitive status. A nadir CD4+ less than 322, current CD4+ at least 136 and undetectable viral load were associated with worsening of cognitive status if CSF citrate was low or creatinine was high. However, if the same cut-off values for nadir and current CD4+ were applied to patients with a detectable plasma viral load, then off antiretrovirals or elevated CSF glutamine content were associated with a worsening phenotype. Thus, nadir and current CD4+ cell counts contributed to prognosis for worsening in all patients (consistent with previous studies [48,49]), but the metabolic features associated with worsening were dependent on a detectable or undetectable viral load. This combined modelling approach identified worsening patients with a predictive accuracy of 86%, a sensitivity of 96% and specificity of 76%.
Improving cognitive status
Our CSF metabolic results suggest that adaptation to anaerobic glycolysis was associated with improving cognitive status in HIV-infected patients (Fig. 4). Decreased glucose and acetate with increased lactate were associated with improving cognitive status, and is consistent with a shift from aerobic to anaerobic glycolysis (fuelled by lactate). Neurons use both glucose and lactate as an energy source. Whether glucose or lactate is the primary energy source for neurons has been long debated [50–52]. Lactate is preferentially produced by astrocytes, and is provided to neurons through the astrocyte to neuron lactate shuttle [53,54]. This transfer of lactate to neurons is upregulated during periods of increased synaptic activity , or when glucose levels are low . Lactate has been shown to promote synaptic plasticity through induction of plasticity-related genes such as Arc, c-Fos, and Zif268, and promotes norepinephrine release from locus coeruleus neurons . Thus, our current findings suggest that increased lactate production may be at least partially responsible for improvements of cognitive function in HIV-infected patients.
We also observed elevated CSF levels of glutamine in the improving group, suggesting that an increased buffering of glutamate may also contribute to cognitive improvement. Glutamine is a trophic amino acid produced largely in astrocytes by a glutamine synthase mediated conversion of glutamate to glutamine. HIV infection, encephalitis, HIV-gp120, Tat, tumour necrosis factor α and interleukin-1β impair glutamate uptake [59,60], and gp120 is known to reduce glutamine levels and survival of cultured astrocytes . Free radical scavenging with N-acetylcysteine protects astrocytes from gp120 and increases glutamine concentrations . These data suggest that improvements in glial energetics may facilitate recovery of cognitive function by increasing uptake and conversion of glutamate to glutamine in patients with improving cognitive function.
Incorporating clinical data with CSF metabolic results better separated patients with improving cognition and those stably impaired. However, baseline clinical variables were less easily interpreted in this group because most patients in the improving category were impaired at baseline. By incorporating longitudinal changes into the modelling, we found that myo-inositol was a critical branch point where lower levels were associated with improving cognitive function in persons less than 47 years of age, or with higher glutamine levels. However, if myo-inositol was high, then considerably more variables were required including higher glucose, or lower β-glucose and creatine, with consistent or new antiretroviral use for association with improving cognitive function. myo-Inositol is widely considered to be a marker of glial activation, and was found to be elevated in magnetic resonance spectroscopy (MRS) studies of HIV-infected patients with cognitive impairment [62–64] and in CSF . These data suggest that decreased glial activation is most often associated with improving cognitive status, but also that high myo-inositol may associate with improving cognition in a small number of patients (four of 48) if glucose is high or creatine is low in patients with new or stable antiretroviral use. As myo-inositol is produced from glucose (via glucose 6-phosphate), these data suggest that elevated glucose may promote the formation of myo-inositol. As myo-inositol is an important precursor to the inositol phosphates including phosphatidylinositol and related phosphoinositides, increasing glucose with increasing myo-inositol suggests that membrane rebuilding or enhanced inositol second messenger signalling may contribute to improving cognitive status in some patients.
Measurement of CSF energy metabolites may better discern low abundance energy metabolites that are not easily detected or separated by brain imaging techniques. However, a limitation of CSF is that it is not possible to determine the precise brain regions affected. Thus, incorporating CSF metabolic measures with brain imaging may better delineate pathways affected by disease, and predict the trajectory of functional outcomes following intervention. For instance, increased glucose uptake in striatum has been suggested as an early indicator of subclinical neurological involvement , but studies in antiretroviral-treated individuals with undetectable viral loads have found varying degrees of reduced glucose uptake in the medial frontal gyrus , and evidence for a small but consistent age-related reduction of glucose uptake in the anterior cingulate cortex, but not in other brain regions . Likewise, creatine, choline, N-acetylaspartate, glutamate-containing and glutamine-containing compounds appear to show annual decreases in multiple brain regions of HIV-infected patients on stable cART [69,70]. Neurocognitive decline appears to be specifically associated with reduced glutamate and glutamine containing compounds in multiple brain regions including frontal white and grey matter, basal ganglia and parietal grey matter [69,70]. These disease- associated decreases in glutamate and glutamine were correlated with deficits in executive function, motor and psychomotor speed, attention and working memory . Our results support these MRS findings, and further suggest that CSF energy metabolites may contribute critical details useful to identify underlying metabolic alterations associated with changes in cognitive function.
Longitudinal changes in the cognitive status of HIV-infected patients were associated with levels of particular CSF energy metabolites. The patterns of change in these CSF metabolites suggested that worsening cognitive status was associated with increased rates of glycolysis with marked accumulations of the TCA cycle intermediates acetate and citrate. Improving cognitive status was accompanied by a shift to anaerobic glycolysis as evidenced by marked increases of lactate. Future work is required to validate these results and to determine whether CSF energy metabolites could be used as diagnostic or prognostic indicators for HAND, and to further elucidate the underlying mechanisms and cellular implications of these metabolic conversions.
Author contributions: N.J.H., J.C.M. and D.C.A. conceived and designed the study. A.M.D. and T.D.W.C. performed the NMR experiments and subsequent analysis. R.D. performed all the recursive partitioning analysis. M.M.M. performed all the linear regression analysis. D.F., D.R., I.G., T.M. and S.L. classified the patients as part of the ARC biomarker study.
Source of funding: These studies were supported by NIH grants R01MH077542, R01MH096636, R03MH103985 to NJH, R01 AG37526 to MMM, and PO1MH075673 to JCM, and by a Capacity Building Studentship to AMD.
Conflicts of interest
There are no conflicts of interest.
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