Untargeted metabolomics based on LC-MS and GC-MS reveal metabolic reprogramming and putative biomarkers in...

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder with unknown etiology. The absence of reliable biochemical and imaging markers often delays diagnosis and limits treatment effectiveness. As metabolic reprogramming is increasingly recognized as a hallmark of ALS, a comprehensive untargeted metabolomics analysis was employed to identify critical metabolic perturbations in ALS and explore novel candidate biomarkers with potential utility in clinical diagnosis.

Plasma from two independent cohorts comprising 399 participants (170 ALS patients, 200 healthy controls, and 29 ALS-unrelated neurological disease controls) was included. Cohort 1 was recruited from Shanghai Jiao Tong University School of Medicine Affiliated Ruijin Hospital (April 2020-September 2022), and cohort 2 from Sichuan Academy of Sciences-Sichuan Provincial Hospital, Ruijin Hospital, Shanghai Jiaotong University Affiliated Sixth People's Hospital, and The First Affiliated Hospital of Dalian Medical University (October 2022-February 2023). Gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS)-based metabolomics approaches were used to identify metabolic alterations and potential diagnostic biomarkers for ALS. Complementary multivariable and univariable statistical approaches were applied to characterize disease-specific metabolic reprogramming in ALS. In addition, the receiver operating characteristic (ROC) curve was used to assess the discriminatory power of differential metabolites, and binary logistic regression analysis was used to construct a multivariate biomarker model.

Metabolic changes of ALS were mainly observed in amino acids, fatty acyls , and purines. Inosine and hypoxanthine were found to be the most significantly and critically dysregulated metabolites in ALS. Aminoacyl-transfer ribonucleic acid (tRNA) biosynthesis and amino acid metabolism were regarded as the most significantly perturbed pathways. Across both cohorts, 26 metabolites were consistently changed. Notably, a biomarker panel comprising hypoxanthine, inosine, and trigonelline was constructed using binary logistic regression, achieving excellent diagnostic performance in distinguishing ALS from controls, with an area under the ROC curve of 0.982 in cohort 1 (sensitivity 0.970, specificity 0.940) and 0.934 in cohort 2 (sensitivity 0.942, specificity 0.791).

The disturbed pathways and biomarker candidates identified in this study may provide novel insights into ALS pathogenesis and improve diagnostic strategies.