Uncovering metabolic dysregulation in schizophrenia and cannabis use disorder through untargeted plasma lipidomics

Abstract

Cannabis use disorder affects up to 42% of individuals with schizophrenia, correlating with earlier onset, increased positive symptoms, and more frequent hospitalizations. This study employed an untargeted lipidomics approach to identify biomarkers in plasma samples from subjects with schizophrenia, cannabis use disorder, or both (dual diagnosis), aiming to elucidate the metabolic underpinnings of cannabis abuse and schizophrenia development. The use of liquid chromatography-high resolution mass spectrometry enabled the annotation of 119 metabolites, with the highest identification confidence level achieved for 16 compounds. Notably, a marked reduction in acylcarnitines, including octanoylcarnitine and decanoylcarnitine, was observed across all patient groups compared to controls. In cannabis use disorder patients, N-acyl amino acids (NAAAs), particularly N-palmitoyl threonine and N-palmitoyl serine, showed a strong downregulation, a pattern also seen in schizophrenia and dual diagnosis patients. Conversely, elevated levels of 7-dehydrodesmosterol were detected in schizophrenia and dual diagnosis patients relative to controls. These findings suggest a potential link between metabolic disruptions and the pathophysiology of both disorders. The untargeted lipidomics approach offers a powerful tool to identify novel biomarkers, enhancing our understanding of the biological relationship between cannabis abuse and schizophrenia, and paving the way for future therapeutic strategies targeting metabolic pathways in these conditions.

Keywords: 7-dehydrodesmosterol; Acylcarnitines; Biomarkers; Cannabis Use Disorder; Lipidomics; N-acyl amino acids; Plasma; Schizophrenia.

Fig. 1

OPLS-DA score plots showing metabolic clustering of (a) CUD (n = 24), (d) DUAL (n = 12) or (g) SZ (n = 18) patients compared to controls. Permutation analysis plotting RY2 and Q2 from 1000 permutation tests in the three OPLS-DA models (b) CUD (n = 24), (e) DUAL (n = 12) or (h) SZ (n = 18). Variable importance in projection (VIP) scores showing the first 15 metabolites that allowed discrimination of groups in the three OPLS-DA models (c) CUD (n = 24), (f) DUAL (n = 12) or (i) SZ (n = 18). Statistical significance was set at p < 0.05.

Fig. 2

Volcano plots showing significant metabolic changes (FC > 2, p < 0.05) in (a) CUD (n = 24), (b) DUAL (n = 12) or (c) SZ (n = 18) patients compared to controls.

Fig. 3

Levels of discriminating metabolites selected by the OPLS-DA models in SZ (green, n = 18), CUD (blue, n = 24) and DUAL (orange, n = 12) groups versus controls (grey, n = 50). . Individual values and mean. One-way ANOVA with Tukey’s post-hoc corrections.*p < 0.05; **p < 0.005; ***p < 0.001; ****p < 0.0001.