Decreased cerebrospinal fluid kynurenic acid in epileptic spasms: A biomarker of response to corticosteroids

Psychotomimetic compensation versus sensitization

It is a paradox that psychotomimetic drugs can relieve symptoms that increase risk of and cooccur with psychosis, such as attention and motivational deficits (e.g., amphetamines), pain (e.g., cannabis) and symptoms of depression (e.g., psychedelics, dissociatives). We introduce the ideas of psychotomimetic compensation and psychotomimetic sensitization to explain this paradox. Psychotomimetic compensation refers to a short-term stressor or drug-induced compensation against stress that is facilitated by engagement of neurotransmitter/modulator systems (endocannabinoid, serotonergic, glutamatergic and dopaminergic) that mediate the effects of common psychotomimetic drugs. Psychotomimetic sensitization occurs after repeated exposure to stress and/or drugs and is evidenced by the gradual intensification and increase of psychotic-like experiences over time. Theoretical and practical implications of this model are discussed.

Metabolomics identifies and validates serum androstenedione as novel biomarker for diagnosing primary angle closure glaucoma and predicting the visual field progression

Background

Primary angle closure glaucoma (PACG) is the leading cause of irreversible blindness in Asia, and no reliable, effective diagnostic, and predictive biomarkers are used in clinical routines. A growing body of evidence shows metabolic alterations in patients with glaucoma. We aimed to develop and validate potential metabolite biomarkers to diagnose and predict the visual field progression of PACG.

Methods

Here, we used a five-phase (discovery phase, validation phase 1, validation phase 2, supplementary phase, and cohort phase) multicenter (EENT hospital, Shanghai Xuhui Central Hospital), cross-sectional, prospective cohort study designed to perform widely targeted metabolomics and chemiluminescence immunoassay to determine candidate biomarkers. Five machine learning (random forest, support vector machine, lasso, K-nearest neighbor, and GaussianNaive Bayes [NB]) approaches were used to identify an optimal algorithm. The discrimination ability was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration was assessed by Hosmer-Lemeshow tests and calibration plots.

Results

Studied serum samples were collected from 616 participants, and 1464 metabolites were identified. Machine learning algorithm determines that androstenedione exhibited excellent discrimination and acceptable calibration in discriminating PACG across the discovery phase (discovery set 1, AUCs=1.0 [95% CI, 1.00-1.00]; discovery set 2, AUCs = 0.85 [95% CI, 0.80-0.90]) and validation phases (internal validation, AUCs = 0.86 [95% CI, 0.81-0.91]; external validation, AUCs = 0.87 [95% CI, 0.80-0.95]). Androstenedione also exhibited a higher AUC (0.92-0.98) to discriminate the severity of PACG. In the supplemental phase, serum androstenedione levels were consistent with those in aqueous humor (r=0.82, p=0.038) and significantly (p=0.021) decreased after treatment. Further, cohort phase demonstrates that higher baseline androstenedione levels (hazard ratio = 2.71 [95% CI: 1.199-6.104], p=0.017) were associated with faster visual field progression.

Conclusions

Our study identifies serum androstenedione as a potential biomarker for diagnosing PACG and indicating visual field progression.

Funding

This work was supported by Youth Medical Talents - Clinical Laboratory Practitioner Program (2022-65), the National Natural Science Foundation of China (82302582), Shanghai Municipal Health Commission Project (20224Y0317), and Higher Education Industry-Academic-Research Innovation Fund of China (2023JQ006).

Critical Factors in Sample Collection and Preparation for Clinical Metabolomics of Underexplored Biological Specimens

This review article compiles critical pre-analytical factors for sample collection and extraction of eight uncommon or underexplored biological specimens (human breast milk, ocular fluids, sebum, seminal plasma, sweat, hair, saliva, and cerebrospinal fluid) under the perspective of clinical metabolomics. These samples are interesting for metabolomics studies as they reflect the status of living organisms and can be applied for diagnostic purposes and biomarker discovery. Pre-collection and collection procedures are critical, requiring protocols to be standardized to avoid contamination and bias. Such procedures must consider cleaning the collection area, sample stimulation, diet, and food and drug intake, among other factors that impact the lack of homogeneity of the sample group. Precipitation of proteins and removal of salts and cell debris are the most used sample preparation procedures. This review intends to provide a global view of the practical aspects that most impact results, serving as a starting point for the designing of metabolomic experiments.

Abnormalities in Clostridioides and related metabolites before ACTH treatment may be associated with its efficacy in patients with infantile epileptic spasm syndrome

OBJECTIVE

Adrenocorticotropic hormone (ACTH) is the first-line treatment of infantile epileptic spasm syndrome (IESS). Its reported effectiveness varies, and our current understanding regarding the role of gut microbiota composition in IESS treatment response is limited. This study assessed the microbiome-metabolome association to understand the role and mechanism of gut microbiota composition in IESS treatment outcomes.

METHODS

Children with IESS undergoing ACTH treatment were enrolled. Pre-treatment stool and serum samples were collected for 16S rRNA gene sequencing and liquid chromatography-tandem mass spectrometry, respectively. The children were divided into "responsive" and "non-responsive" groups, and gut microbiota and serum metabolome differences were analyzed.

RESULTS

Of the 30 patients with IESS, 14 responded to ACTH and 16 did not. The "non-responsive" group had larger maleficent Clostridioides and Peptoclostridium_phage_p630P populations (linear discriminant analysis >2; false discovery rate q < 0.05). Ten metabolites were upregulated (e.g., xanthurenic acid) and 15 were downregulated (e.g., vanillylmandelic acid) (p < 0.05). Association analysis of the gut microbiome and serum metabolome revealed that Clostridioides and Peptoclostridium_phage_p630P2 were positively correlated with linoleic and xanthurenic acids, while Clostridioides was negatively correlated with vanillylmandelic acid (p < 0.05). A classifier using differential gut bacteria and metabolites achieved an area under the receiver operating characteristic curve of 0.906 to distinguish responders from non-responders.

CONCLUSION

This study found significant differences in pre-treatment gut microbiota and serum metabolome between children with IESS who responded to ACTH and those who did not. Additional exploration may provide valuable information for treatment selection and potential interventions. Our results suggest that varying ACTH responses in patients with IESS may be associated with increased gut Clostridioides bacteria and kynurenine pathway alteration, but additional experiments are needed to verify this association.

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.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.