Cerebrospinal fluid and plasma metabolomics of acute endurance exercise.

Metabolomics has emerged as a powerful new tool in precision medicine. No studies have yet been published on the metabolomic changes in cerebrospinal fluid (CSF) produced by acute endurance exercise. CSF and plasma were collected from 19 young active adults (13 males and 6 females) before and 60 min after a 90-min monitored outdoor run. The median age, BMI, and VO2 max of subjects was 25 years (IQR 22-31), 23.2 kg/m2 (IQR 21.7-24.5), and 47 ml/kg/min (IQR 38-51), respectively. Targeted, broad-spectrum metabolomics was performed by liquid chromatography, tandem mass spectrometry (LC-MS/MS). In the CSF, purines and pyrimidines accounted for 32% of the metabolic impact after acute endurance exercise. Branch chain amino acids, amino acid neurotransmitters, fatty acid oxidation, phospholipids, and Krebs cycle metabolites traceable to mitochondrial function accounted for another 52% of the changes. A narrow but important channel of metabolic communication was identified between the brain and body by correlation network analysis. By comparing these results to previous work in experimental animal models, we found that over 80% of the changes in the CSF correlated with a cascade of mitochondrial and metabolic changes produced by ATP signaling. ATP is released as a co-neurotransmitter and neuromodulator at every synapse studied to date. By regulating brain mitochondrial function, ATP release was identified as an early step in the kinetic cascade of layered benefits produced by endurance exercise.

Metabolomic profiling of cerebrospinal fluid reveals an early diagnostic model for central nervous system involvement in acute lymphoblastic leukaemia.

The pathogenesis of central nervous system involvement (CNSI) in patients with acute lymphoblastic leukaemia (ALL) remains unclear and a robust biomarker of early diagnosis is missing. An untargeted cerebrospinal fluid (CSF) metabolomics analysis was performed to identify independent risk biomarkers that could diagnose CNSI at the early stage. Thirty-three significantly altered metabolites between ALL patients with and without CNSI were identified, and a CNSI evaluation score (CES) was constructed to predict the risk of CNSI based on three independent risk factors (8-hydroxyguanosine, l-phenylalanine and hypoxanthine). This predictive model could diagnose CNSI with positive prediction values of 95.9% and 85.6% in the training and validation sets respectively. Moreover, CES score increased with the elevated level of central nervous system (CNSI) involvement. In addition, we validated this model by tracking the changes in CES at different stages of CNSI, including before CNSI and during CNSI, and in remission after CNSI. The CES showed good ability to predict the progress of CNSI. Finally, we constructed a nomogram to predict the risk of CNSI in clinical practice, which performed well compared with observed probability. This unique CSF metabolomics study may help us understand the pathogenesis of CNSI, diagnose CNSI at the early stage, and sequentially achieve personalized precision treatment.

Cerebrospinal Fluid Metabolomic Pattern of Different Pituitary Stalk Lesions.

OBJECTIVE: To describe the cerebrospinal fluid (CSF) metabolomic pattern of pituitary stalk lesions. METHODS: CSF was collected from patients with different pituitary stalk lesions treated at Peking Union Medical College Hospital: germ cell tumor (GCT, n = 27); hypophysitis (n = 10); and Langerhans cell histiocytosis (LCH) or Erdheim-Chester disease (ECD) (LCH + ECD, n = 10). The CSF metabolome profiles were characterized by liquid chromatography-mass spectrometry (LC-MS). RESULTS: There were 44 metabolites that significantly differed between patients with GCT and those with hypophysitis (P < .05). Between patients with GCT with CSF level of beta subunit of human chorionic gonadotrophin (ß-hCG) < 5 mIU/mL and those with hypophysitis, there were 15 differential metabolites (P < .05, fold change > 1.5 or < 1/1.5). All of the metabolites had an area under the curve (AUC) above 0.7. There were 9 metabolites that significantly differed between patients with GCT and those with LCH + ECD (P < .05) and 7 metabolites had significant differences between GCT (CSF ß-hCG < 5 mIU/mL) and LCH + ECD (P < .05, fold change > 1.5 or < 1/1.5). We found 6 metabolites that were significantly different between patients with hypophysitis and those with LCH + ECD (P < .05) and 5 of these had fold change more than 1.5 or less than 1/1.5. Three metabolites, 5-deoxydiplosporin, cloversaponin I, and phytosphingosine, showed excellent capabilities to differentiate the 3 disease categories. Furthermore, we identified 67 metabolites associated with clinical test results (ρ > 0.2, P < .05) and 29 metabolites showed strong correlation (ρ > 0.4, P < .05). CONCLUSION: Our study is the first to systematically investigate the metabolomics of CSF in different pituitary stalk lesions. CSF metabolomics is a useful strategy for biomarker discovery.

CSF neopterin, quinolinic acid and kynurenine/tryptophan ratio are biomarkers of active neuroinflammation.

BACKGROUND: Defining the presence of acute and chronic brain inflammation remains a challenge to clinicians due to the heterogeneity of clinical presentations and aetiologies. However, defining the presence of neuroinflammation, and monitoring the effects of therapy is important given its reversible and potentially damaging nature. We investigated the utility of CSF metabolites in the diagnosis of primary neuroinflammatory disorders such as encephalitis and explored the potential pathogenic role of inflammation in epilepsy. METHODS: Cerebrospinal fluid (CSF) collected from 341 paediatric patients (169 males, median age 5.8 years, range 0.1-17.1) were examined. The patients were separated into a primary inflammatory disorder group (n = 90) and epilepsy group (n = 80), who were compared with three control groups including neurogenetic and structural (n = 76), neurodevelopmental disorders, psychiatric and functional neurological disorders (n = 63), and headache (n = 32). FINDINGS: There were statistically significant increases of CSF neopterin, kynurenine, quinolinic acid and kynurenine/tryptophan ratio (KYN/TRP) in the inflammation group compared to all control groups (all p < 0.0003). As biomarkers, at thresholds with 95% specificity, CSF neopterin had the best sensitivity for defining neuroinflammation (82%, CI 73-89), then quinolinic acid (57%, CI 47-67), KYN/TRP ratio (47%, CI 36-56) and kynurenine (37%, CI 28-48). CSF pleocytosis had sensitivity of 53%, CI 42-64). The area under the receiver operating characteristic curve (ROC AUC) of CSF neopterin (94.4% CI 91.0-97.7%) was superior to that of CSF pleocytosis (84.9% CI 79.5-90.4%) (p = 0.005). CSF kynurenic acid/kynurenine ratio (KYNA/KYN) was statistically decreased in the epilepsy group compared to all control groups (all p ≤ 0.0003), which was evident in most epilepsy subgroups. INTERPRETATION: Here we show that CSF neopterin, kynurenine, quinolinic acid and KYN/TRP are useful diagnostic and monitoring biomarkers of neuroinflammation. These findings provide biological insights into the role of inflammatory metabolism in neurological disorders and provide diagnostic and therapeutic opportunities for improved management of neurological diseases. FUNDING: Financial support for the study was granted by Dale NHMRC Investigator grant APP1193648, University of Sydney, Petre Foundation, Cerebral Palsy Alliance and Department of Biochemistry at the Children's Hospital at Westmead. Prof Guillemin is funded by NHMRC Investigator grant APP 1176660 and Macquarie University.