The serum metabolomic profile of a distinct, inflammatory subtype of acute psychosis.

A range of studies suggest that a proportion of psychosis may have an autoimmune basis, but this has not translated through into clinical practice-there is no biochemical test able to accurately identify psychosis resulting from an underlying inflammatory cause. Such a test would be an important step towards identifying who might require different treatments and have the potential to improve outcomes for patients. To identify novel subgroups within patients with acute psychosis we measured the serum nuclear magnetic resonance (NMR) metabolite profiles of 75 patients who had identified antibodies (anti-glycine receptor [GlyR], voltage-gated potassium channel [VGKC], Contactin-associated protein-like 2 [CASPR2], leucine-rich glioma inactivated 1 [LGI1], N-methyl-D-aspartate receptor [NMDAR] antibody) and 70 antibody negative patients matched for age, gender, and ethnicity. Clinical symptoms were assessed using the positive and negative syndrome scale (PANSS). Unsupervised principal component analysis identified two distinct biochemical signatures within the cohort. Orthogonal partial least squared discriminatory analysis revealed that the serum metabolomes of NMDAR, LGI1, and CASPR2 antibody psychosis patients were indistinct from the antibody negative control group while VGKC and GlyR antibody patients had significantly decreased lipoprotein fatty acids and increased amino acid concentrations. Furthermore, these patients had more severe presentation with higher PANSS scores than either the antibody negative controls or the NMDAR, LGI1, and CASPR2 antibody groups. These results suggest that a proportion of patients with acute psychosis have a distinct clinical and biochemical phenotype that may indicate an inflammatory subtype.

Distinct plasma metabolomic signatures differentiate autoimmune encephalitis from drug-resistant epilepsy.

OBJECTIVE: Differentiating forms of autoimmune encephalitis (AE) from other causes of seizures helps expedite immunotherapies in AE patients and informs studies regarding their contrasting pathophysiology. We aimed to investigate whether and how Nuclear Magnetic Resonance (NMR)-based metabolomics could differentiate AE from drug-resistant epilepsy (DRE), and stratify AE subtypes. METHODS: This study recruited 238 patients: 162 with DRE and 76 AE, including 27 with contactin-associated protein-like 2 (CASPR2), 29 with leucine-rich glioma inactivated 1 (LGI1) and 20 with N-methyl-d-aspartate receptor (NMDAR) antibodies. Plasma samples across the groups were analyzed using NMR spectroscopy and compared with multivariate statistical techniques, such as orthogonal partial least squares discriminant analysis (OPLS-DA). RESULTS: The OPLS-DA model successfully distinguished AE from DRE patients with a high predictive accuracy of 87.0 ± 3.1% (87.9 ± 3.4% sensitivity and 86.3 ± 3.6% specificity). Further, pairwise OPLS-DA models were able to stratify the three AE subtypes. Plasma metabolomic signatures of AE included decreased high-density lipoprotein (HDL, -(CH2)n-, -CH3), phosphatidylcholine and albumin (lysyl moiety). AE subtype-specific metabolomic signatures were also observed, with increased lactate in CASPR2, increased lactate, glucose, and decreased unsaturated fatty acids (UFA, -CH2CH=) in LGI1, and increased glycoprotein A (GlycA) in NMDAR-antibody patients. INTERPRETATION: This study presents the first non-antibody-based biomarker for differentiating DRE, AE and AE subtypes. These metabolomics signatures underscore the potential relevance of lipid metabolism and glucose regulation in these neurological disorders, offering a promising adjunct to facilitate the diagnosis and therapeutics.