Metabolite Profiling Identifies a Branched Chain Amino Acid Signature in Acute Cardioembolic Stroke
Background and Purpose—There is limited information about changes in metabolism during acute ischemic stroke. The identification of changes in circulating plasma metabolites during cerebral infarction may provide insight into disease pathogenesis and identify novel biomarkers.
Methods—We performed filament occlusion of the middle cerebral artery of Wistar rats and collected plasma and cerebrospinal fluid 2 hours after the onset of ischemia. Plasma samples from control and patients with acute stroke were also analyzed. All samples were examined using liquid chromatography followed by tandem mass spectrometry. Positively charged metabolites, including amino acids, nucleotides, and neurotransmitters, were quantified using electrospray ionization followed by scheduled multiple reaction monitoring.
Results—The concentrations of several metabolites were altered in the setting of cerebral ischemia. We detected a reduction in the branched chain amino acids (valine, leucine, isoleucine) in rat plasma, rat cerebrospinal fluid, and human plasma compared with respective controls (16%, 23%, and 17%, respectively; P<0.01 for each). In patients, lower branched chain amino acids levels also correlated with poor neurological outcome (modified Rankin Scale, 0–2 versus 3–6; P=0.002).
Conclusions—Branched chain amino acids are reduced in ischemic stroke, and the degree of reduction correlates with worse neurological outcome. Whether branched chain amino acids are in a causal pathway or are an epiphenomenon of ischemic stroke remains to be determined.
- cerebrospinal fluid
- liquid chromatography
- mass spectrometry
- transient ischemic attack stroke
The underlying pathogenesis of acute ischemic stroke remains poorly understood, with a paucity of biological insight translating into useful therapy in patients. Metabolomics is an emerging analytic technology for understanding disease pathogenesis that can be applied to both animal models and patient blood samples. It therefore represents an attractive translational tool to link the biology of model systems to the pathophysiology in patients. Using either nuclear magnetic resonance spectroscopy or mass spectrometry (MS),1 metabolomics can measure numerous small metabolites simultaneously.2 MS-based profiling methods include gas chromatography–MS and liquid chromatography coupled to MS, the most common of which is tandem MS (LC-MS/MS).3 Approaches that use LC-MS/MS are increasingly used because of their sensitivity, flexibility, and quantitative capability for small molecule detection.2
Metabolomic profiling has found application in other forms of metabolic stress,4 including intense exercise,5 myocardial ischemia,6 myocardial infarction,7 and diabetes mellitus,8–10 but little is known about metabolite changes in the setting of stroke. A common strategy used, in prior metabolomics studies, was to compare the metabolome within subjects, before and after the exposure. However, baseline blood sampling is not feasible in patients with acute stroke. We therefore sought to establish a metabolomic profile in an animal model of ischemic stroke in which baseline sampling is possible, and then integrate the findings with profiling in individuals with acute ischemic stroke. Using a rodent filament occlusion model, we first identified potential candidates whose plasma and cerebrospinal fluid (CFS) were altered. We then evaluated those candidates in an analogous patient cohort in which plasma samples were collected in the acute setting. We hypothesized that we could detect a specific pattern of circulating metabolites that would reflect the chain of metabolic events that occur during cerebral ischemia. Our goal was to apply this new systematic tool as a first step to better understand the biology and pathogenesis of acute ischemic stroke. In doing so, we also explored whether these candidates might serve as potential biomarkers for diagnosis or prognosis.11–13
Adult male Wistar rats weighing 275 to 350 g were obtained from Charles River Laboratories (Wilmington, MA). Animals were housed with free access to food and water. The evening before surgery, animals were given nothing per oral to avoid the effect of dietary intake on circulating metabolites. Transient filament occlusion was performed using a 4-0 siliconized suture (Doccol Corp, Sharon, MA) according to standard methods (Methods in the online-only Data Supplement).14,15 Approximately 250 μL of plasma was withdrawn at baseline and at 2 hours after ischemia onset.
CSF (≈50 μL) was collected from the cisterna magna at 2 hours after ischemia, using a 27-gauge winged needle set attached to a 1 cm3 syringe.16 Animals were allowed to recover, and at 24 hours after ischemia, brains were harvested for 2,3,5-triphenyltetrazolium chloride staining to assess the size of stroke.17 All experiments were approved under an institutionally approved protocol in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals.
We analyzed EDTA-containing plasma samples collected at a single center, as part of a prospective 2 center biomarker study of acute ischemic stroke (Specialized Programs of Translational Research in Acute Stroke [SPOTRIAS] Network). The SPOTRIAS biomarker study enrolled consecutive patients aged ≥18 years between January 2007 and April 2010, who presented to the Massachusetts General Hospital Emergency Department within 9 hours of symptom onset, with symptoms consistent with ischemic stroke (see Methods in the online-only Data Supplement for additional details of the cohort and the patient data and imaging collection). Ischemic stroke was defined as acute-onset focal neurological deficit with neuroimaging evidence of infarction, or symptom duration >24 hours in the setting of negative diffusion-weighted MRI. Transient ischemic attack was defined as resolution of neurological symptoms within 24 hours that were consistent with a vascular ischemic event (n=18). The designation of “not a stroke” was reserved for subjects with a negative diffusion-weighted MRI who also had an alternative diagnosis for neurological symptoms at discharge (n=14). All subjects or their healthcare proxy provided informed consent, and this study was approved by the local institutional review board.
We applied a case-control design to mirror the animal modeling experiments. We defined 3 groups from the SPOTRIAS biomarker cohort: control, mild, and severe stroke groups. Controls included all subjects with a final diagnosis of transient ischemic attack or absence of stroke (n=32). A similar sized group of mild ischemic stroke was selected from cardioembolic stroke subjects, and 22 sequential subjects with a National Institutes of Health Stroke Scale (NIHSS) ≥4 were used. We also selected sequential subjects with severe cardioembolic stroke, defined as those with an NIHSS ≥15 (n=30). All subjects or their healthcare proxy provided informed consent, and this study was approved by the local institutional review board.
High Performance Liquid Chromatography and Tandem MS
EDTA blood samples were collected and immediately centrifuged to separate cellular material. Aliquots of plasma supernatant were frozen on dry ice and stored at −80°C until analysis. Plasma samples (10 μL) were deproteinized with 90 μL acetonitrile/methanol (3:1; v/v) containing internal standards (valine-d8 [Sigma-Aldrich] and phenylalanine-d8 [Cambridge Isotope Laboratories]). After centrifugation, the extracts were subjected to normal phase hydrophilic interaction chromatography. The chromatography system consisted high throughput screen prep and load autosampler (Leap Technologies, Carrboro, NC) connected to a high performance liquid chromatography pump (1200 Series, Agilent, Santa Clara, CA). MS data were acquired using a 4000 QTRAP triple quadrupole mass spectrometer (Applied Biosystems/Sciex, Framingham, MA) equipped with an electrospray ionization source. Positively charged amino acids, nucleotides, and neurotransmitters were selected for targeted MS/MS analysis using selected multiple reaction monitoring conditions determined previously using reference standards.,6,7
A total of 68 endogenous metabolites were monitored and detected for each sample. The metabolites were selected on the basis of a broad representation of diverse metabolic pathways as possible, balanced against compatibility with the chromatography and MS ionization method. Deuterated internal standards (valine-d8 and phenylalanine-d8, Cambridge Isotope Laboratories (Andover, MA)) were included in each sample to monitor for quality control. Any sample with internal standard values ≥2 SD were excluded from peak integration and further analysis. In addition, pooled plasma samples were interspersed within each analytic run at standardized intervals, enabling the monitoring and correction for temporal drift in MS performance. Each of these samples were prepared, extracted, and processed as separate 10 μL aliquots from a larger pool of normal human plasma. Replicate injections of pooled plasma demonstrated that 50% of the analytes had a coefficient of variation ≤5% (including the branched chain amino acids [BCAA]), 69% of the analytes had a coefficient of variation ≤10%, and 91% had coefficient of variation ≤20%, which is consistent with prior studies.9
Differences in clinical and laboratory continuous variables were compared using Student t test or Mann–Whitney test, as appropriate. Categorical variables were compared using Fisher exact test. For the metabolite analysis in the animal samples, we used an uncorrected P value threshold of 0.05, using Mann–Whitney or Student t test, depending on data normality. In this exploratory phase, no correction for multiple comparisons was made.
In the human cohort analysis, we used a similar approach to our prior studies5 and applied the Benjamini–Hochberg procedure18 to limit the false discovery rate to q<0.1, which corresponded to a threshold of P<0.015. This would be expected to yield ≈1 false-positive discovery in 68 metabolites analyzed, assuming independent hypotheses. Moreover, this threshold also approximates the Bonferroni correction of the combined probability between the discovery cohort (P<0.05) and the human validation cohort (P<0.015) (ie, 0.05×0.015=7.5×10–4, whereas Bonferroni correction=0.05/68=7.4×10–4). Although many metabolites were associated with predefined groups (eg, amino acids, tryptophan derivatives, nucleotide metabolites, etc.), this is a conservative estimate because the number of independent tests was substantially lower than the nominal ones. Statistical analyses were performed using the STATA statistical software (release 12) or JMP 10 Pro (SAS Institute, Cary, NC).
To uncover the multivariate structure within the human data set, we performed principal component analysis and partial least-squares discriminant analysis using MetaboAnalyst 2.0 (Edmonton, BC, Canada).19 Because each method provides slightly different insight into high-dimensional data, we performed to highlight the metabolites in common (see Methods in the online-only Data Supplement for further details).
Using LC-MS/MS, we first examined serial blood samples after filament occlusion in a rat model of ischemic stroke. We collected plasma at baseline and 2 hours after ischemia, as well as CSF at the 2-hour time point. In pilot experiments, the placement of a laser Doppler flowmetry probe led to poor recovery of CSF (data not shown). Exploiting the variability in stroke volume that would occur in the absence of Doppler flowmetry, we designed our experiment as a comparison among sham, small stroke, and large stroke animal cohorts. Of 23 animals, 2 died acutely and the volume of infarct could not be determined. Another animal assigned to the middle cerebral artery occlusion group had no infarction at 24 hours and was therefore excluded. The remaining 20 animals were included in the analysis: 7 sham-operated animals, 6 animals with small infarction (stroke volume 9±5%), and 7 animals with large infarction (stroke volume 29±5%; Table I in the online-only Data Supplement).
We measured a total of 68 metabolites in baseline and 2-hour follow-up plasma samples, and results were analyzed as a percent change from baseline, which adjusts for within animal variation. To eliminate any nonspecific effects of the operative technique, we compared percent metabolite changes in stroke animals to those in sham-operated animals. From baseline to 2 hours after stroke, there was a significant decrease in the concentration of BCAA leucine, isoleucine, and valine in the large stroke group (P=0.003, 0.01, and 0.04, respectively). BCAA are coordinately regulated, and the levels change in conjunction with each other.20 Accordingly, a composite measurement of the BCAA showed a 16±6% decrease in large stroke (P=1×10−5; Figure 1D) and a nonsignificant trend in small stroke. Several other metabolites were altered in a dose-dependent manner in small and large stroke. These included stepwise increases in xanthosine (+57%; P<0.001), carnosine (+71%; P<0.005), and glutamate (+40%; P=0.01), and decreases in niacinamide (−31%; P=0.02) and phenylalanine (−18%; P<0.01) relative to sham-operated animals.
We also measured the same metabolites in the CSF obtained through cisterna magna puncture at 2 hours after onset of ischemia. Because the concentration of most metabolites in CSF is lower than in plasma, we excluded any CSF samples with visible blood contamination (Methods and Figure I in the online-only Data Supplement). Figure 2A through 2D shows that the individual BCAAs had a consistent trend toward a decrease (leucine −21%, P=0.06; isoleucine −23%, P=0.14; valine −22%, P=0.11). Moreover, a composite of BCAA demonstrated a decrease of 23±9% compared with sham CSF (n=5 for each group; P<0.005). Other significantly altered CSF metabolites included an accumulation of xanthosine (102%, P=0.01) and lysine (18%, P=0.02).
On the basis of the animal studies, concordant metabolite changes between plasma and CSF included valine, leucine, isoleucine, and xanthosine. We next evaluated whether these candidate metabolites were altered in the plasma of patients with acute stroke to determine whether these metabolite changes represented a common alteration. We obtained plasma samples from a cohort of patients in whom blood was collected acutely, shortly after presentation to the emergency department. We selected a subset of subjects to coincide with the animal modeling design, which included a control group (patients with a diagnosis of transient ischemic attack or absence of stroke), a group with mild stroke (patients with an NIHSS 4–5), and a severe stroke group (NIHSS 15–19). To limit potential heterogeneity, we focused on subjects with a cardioembolic cause of stroke. The clinical characteristics of the cohort are listed in Table 1. As would be expected, the stroke group had an older age and higher rates of atrial fibrillation compared with the control group. In addition, the large stroke group had a higher acute stroke volume, higher acute NIHSS, and worse 3-month neurological outcome as compared with the small or control groups.
We analyzed plasma samples obtained within 6±2 hours from the last seen well time, using our metabolomics method. Heat map correlation analysis confirmed a close association of the BCAAs (Figure 3, top right), consistent with the animal modeling data and with the known coordinated metabolism of these amino acids.20 Analysis of individual metabolites showed that leucine, isoleucine, and valine were all decreased in stroke compared with control, and to a greater extent in large compared with small stroke (P<0.01 for each; Figure 4). Similarly the composite BCAA score demonstrated a 9±17% decrease in small stroke (P=0.03) and a 17±23% decrease in large stroke (P=1.1×10−5). Table 2 provides a complete list of all metabolites that were altered in the setting of ischemia when compared with control patients. In addition to novel metabolites, we found that glucose showed a significant increase in stroke compared with control, which is concordant with the well-described phenomenon of acute stress hyperglycemia.21–23
To further simplify the high-dimensional metabolomics data, we next performed principal component analysis. This approach consolidates data into fewer metabolite clusters, which maximally explain the variance in the data.19 Intriguingly, the first principal component (principal component, which explained 20% variance in the data; Figure II in the online-only Data Supplement for score and loading plots) contained the BCAA metabolites. In addition to leucine, valine, and isoleucine, the first principal component also included tyrosine, lysine, and methionine. Comparing the individual subjects’ scores, the first principal component also distinguished cases from controls (P=0.020 comparing control versus all stroke and P=0.011 for control versus large stroke).
Next, we performed partial least-squares discriminant analysis, which is a method of supervised classification that is designed to highlight metabolite differences between cases and controls. This technique is commonly used in metabolomics studies for biomarker discovery because it emphasizes the distinction between the 2 classes.19 The metabolites that contributed the greatest discrimination between stroke and controls were similar to our univariate analysis presented in Table 2. These included the BCAAs, carnitine, threonine, histidine, and glucose (Figure III in the online-only Data Supplement). Validation of the model was confirmed using cross-validation and permutation testing (P<0.01; Figure III in the online-only Data Supplement).19,24
Having confirmed that BCAA were altered acutely in stroke, we next explored its association with imaging and clinical measures. Because the magnitude of BCAA change seemed to correlate with size of stroke in the animal model, we evaluated the correlation between BCAA and diffusion-weighted volume in the patient cohort. There was a nonsignificant trend in association between admission infarct volume and BCAA (r=−0.18; P=0.11). On the contrary, a lower concentration of BCAA was associated with increased age (r=−0.26; P=0.02), female sex (P<0.001), and worse outcome at 3 months (modified Rankin Scale, 3–6; P=0.002). Because age and sex are also recognized predictors of worse neurological outcome,25,26 we explored whether BCAA predicted outcome independently of age and sex. Although the cohort was limited in size and stratified on the basis of stroke severity, we performed exploratory multivariable logistic regression and found that BCAA remained an independent predictor of outcome (P=0.04) after adjusting for age and sex.
Using metabolomics, we have identified specific circulating metabolites that are altered in the setting of cerebral infarction. On the basis of our systematic analysis in a well-controlled animal model and linking those findings to patient samples in the acute setting, we have identified a small and interrelated subset of metabolites. Our data demonstrate a reduction in the concentration of BCAAs that associates with stroke severity and worse neurological outcome. Although our data do not point to an underlying biological mechanism, they focus future experiments on investigating candidate pathways that relate to BCAA. The notion that BCAA play an important role in the metabolic response to disease is supported by evidence of its alteration in other illnesses. For example, BCAA is reduced in critical illnesses, such as sepsis, trauma, and burn injury.27–29 BCAA is also associated with the risk of incident diabetes mellitus9 and can induce insulin resistance,8 further suggesting a role in metabolic homeostasis. Perhaps, most interestingly, BCAA are altered also in heart disease,30 suggesting that these amino acids play a critical role in bioenergetic homeostasis. Whether BCAA represent a novel link between cardiovascular and cardioembolic cerebrovascular diseases requires further investigation.
In addition to their potential role in systemic disease states, BCAA also serve a unique role in the brain.31,32 For example, BCAA are integral to the glutamate/glutamine cycle between astrocytes and neurons, which is critical for the efficient uptake of glutamate during excitatory neuronal signaling.31 Intriguingly, inhibition of the first step of BCAA catabolism with gabapentin reduces brain glutamate concentration.31 Gabapentin has been reported to reduce stroke volume in a rodent model,33 and 1 possibility is that it may do so by limiting glutamate concentration and subsequent excitotoxicity. Although our rodent data showed an accumulation of glutamate, we did not detect a similar change in the patients. Whether this reflects inadequate power or greater complexity in the human cohort requires further study. Alternatively, the reduction in BCAA level may reflect a metabolic pathway leading to consumption or sequestration in a tissue compartment other than blood or CSF. BCAA are also known to have roles in protein metabolism and in catabolic energy metabolism.20 These putative mechanisms are not mutually exclusive, and, indeed, systemic BCAA levels have been shown to influence brain neurotransmitter levels.32 Nevertheless, our data raise the possibility that manipulation of BCAA may influence outcome. Future studies that focus on whether BCAA are causally related to cerebral ischemia, such as through supplementation and pharmacological inhibition, will help determine whether BCAA holds promise as a therapeutic target.
Our analysis in rodents and patients identified additional candidate metabolites, which were not shared in common between the 2 (Table 2 and Results). The similarities and differences between rodent model systems and patients are an area of substantial importance for translational therapy. Metabolomics is a technique that allows direct comparisons between the model systems and patients in a way that was not previously available. Although our findings with BCAA highlight that there are similar biological pathways in rodents and patients, the differences may offer some caution. Nevertheless, our data point to one approach to explore these similarities and differences systemically, both of which are important for novel target discovery. There is little prior metabolomics analysis of stroke, with the exception of an nuclear magnetic resonance–based study in a cohort of lacunar stroke subjects,34 which analyzed blood samples collected within 72 hours of stroke onset. Of the overlapping metabolites in common with our method, valine was diminished in lacunar stroke, although leucine and isoleucine were not.34 The apparent differences may reflect the increased sensitivity of LC-MS/MS compared with nuclear magnetic resonance, differing metabolomes based on stroke subtype, differences in control selection, and potentially in the timing of the blood draw.
Our study has several strengths. We used a carefully controlled model system to establish a metabolite profile and then compared it to a well-phenotyped patient cohort. We used a metabolomics technique that is well validated and possesses excellent quantitative capability and reproducibility. The patient samples were obtained in the hyperacute phase and compared with a control group of stroke mimics. However, there are several limitations to our analysis. We used a targeted metabolomics approach, which identifies a limited set of metabolites rather than a comprehensive list of known and unknown peaks. It is therefore possible that additional metabolite changes occur that we cannot detect with our current method. LC-MS/MS–based metabolomics also has limited throughput capability. Nevertheless, we have selected key sentinel metabolites that are central to several important biochemical pathways, including amino acids, nucleotides, and selected neurotransmitters. Although our data point toward a key role for BCAA in stroke, our correlation and multivariate regression must be interpreted with caution in a small patient cohort. Most importantly, validation in a larger cohort that includes all stroke subtypes with a broad range of stroke severity will be necessary to confirm our findings and determine whether BCAA holds promise as a clinically useful biomarker or a therapeutic target.
Sources of Funding
This study was supported by the Clinical Investigator Training Program: Beth Israel Deaconess Medical Center, Harvard Medical School, in collaboration with Pfizer Inc and Merck and Co (Dr Kimberly), by National Institute of Health (NIH) K23NS076597 (Dr Kimberly), NIH 5P50NS051343-07 (Dr Furie), and NIH R01HL096738 and R01HL098280 (Dr Gerstzen).
Dr Kimberly discloses a research grant (National Institute of Health [NIH]; significant) and Dr Gerszten discloses a research grant (NIH; significant).
Costantino Iadecola, MD, was guest editor for this article.
The online-only Data Supplement is available with this article at http://stroke.ahajournals.org/lookup/suppl/doi:10.1161/STROKEAHA.111.000397/-/DC1.
- Received December 5, 2012.
- Revision received February 22, 2013.
- Accepted February 25, 2013.
- © 2013 American Heart Association, Inc.
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