A metabolomics-based study of serum biomarkers in adolescents with nonsuicidal self-injury

Objective: This study aims to elucidate the metabolite profiles in the serum of adolescents engaging in non-suicidal self-injury (NSSI) by employing high-resolution, non-targeted metabolomics. The objectives include differentiating metabolites between the NSSI group and a normal control group, identifying biomarkers of clinical diagnostic value, and utilising these differential metabolites to pinpoint key metabolic pathways implicated in the pathogenesis of NSSI through bioinformatics analyses. Methods: The sample consisted of 39 NSSI patients, aged 13-22 years, presenting at the outpatient clinic of the Department of Psychiatry, Second Affiliated Hospital of Shandong First Medical University from January 2022 to December 2023, with initial, untreated NSSI. Additionally, 24 healthy adolescents were conscripted from the community. Participants were matched for age, gender, and BMI. Ultra-high performance liquid chromatography mass spectrometry (UPLC-MS) facilitated non-target metabolomic analysis. Multivariate statistical analyses, amalgamating univariate and multivariate approaches, enabled the discrimination of differential metabolites and the extraction of biomarkers. Concurrently, bioinformatics evaluation of these metabolites was undertaken to annotate pertinent metabolic pathways. Results: In cationic mode, 235 differential metabolites were discerned, with 133 upregulated and 102 downregulated in the NSSI cohort. Anionic mode identified 66 differential metabolites; among these, 14 were upregulated and 52 were downregulated. KEGG pathway annotations yielded 311 pathways, encompassing 158 cationic and 153 anionic pathways. Significantly enriched and high-degree KEGG pathways included aromatase deficiency, 17-β hydroxysteroid dehydrogenase III deficiency, nadolol's mechanism of action, timosartan's mechanism of action, androgen and estrogen metabolism, α-linolenic and linoleic acid metabolism in the cationic mode, and nicotinic acid and nicotinamide metabolism in the anionic mode. Substances such as phenylalanine, glycine, aspartic acid, asparagine, threonine, histidine, tyrosine, arginine, isoleucine, proline, N-acetylthreonine, glutamine, organic acids and their derivatives, cyclopropene, glycerophospholipids, fatty acylcarnitines, geldanamycin, and cycloprostenol were paramount in distinguishing NSSI patients from healthy controls and exhibited the highest predictive power. Conclusion: Metabolic perturbations characterise NSSI patients, with elevated or diminished levels of substances like phenylalanine, glycine, aspartic acid, asparagine, threonine, histidine, tyrosine, arginine, isoleucine, proline, N-acetylthreonine, glutamine, and others significantly contributing to this distinction. These findings underscore the potential of metabolic biomarkers in understanding and predicting NSSI.