Serum metabolomics study of anxiety disorder patients based on LC-MS

In the current environment of increasing social pressure, anxiety disorder has become a kind of health problem that needs to be solved urgently. However, the pathological mechanism of anxiety is still unclear, the classification of clinical diagnosis and symptoms is complex, and there is still a lack of biomarkers that can be identified and judged.

This study used LC-MS and non-targeted metabolomics to analyze the clinically collected plasma of 18 samples from anxiety disorder patients and 31 samples from healthy people to screen differential metabolites and perform subsequent metabolic pathway analysis. Binary Logistic regression was used to construct the anxiety disorder diagnosis prediction model and evaluate the prediction efficacy.

The results showed that 22 metabolites were disturbed in the plasma of anxiety patients compared with healthy people. These metabolites mainly participate in 6 metabolic pathways. The combined diagnostic factors 4-Acetamidobutanoate, 3-Hydroxysebacic acid, and Cytosine were used to construct the diagnosis prediction model. The prediction probability of the model is 91.8%, the Youden index is 0.889, the sensitivity is 0.889, and the specificity is 1.000, so the prediction effect is good.

This study preliminarily analyzed and explored the differences between plasma samples from patients with anxiety disorder and healthy individuals, increased the types of potential biomarkers for anxiety disorder, and provided a valuable reference for subsequent research related to anxiety disorder.