Identification of Serum Biomarkers Associated With Emergence Agitation After General Anesthesia in Adult Patients: A Metabolomics Analysis

Xinning Mi; Jingshu Hong; Zhengqian Li; Taotao Liu; Qian Wang; Jiansuo Zhou; Yitong Li; Xiaoxiao Wang; Yi Yuan; Ning Yang; Yongzheng Han; Yang Zhou; Xiangyang Guo; Yue Li; Dengyang Han

doi:10.3389/fmed.2022.828867

Identification of Serum Biomarkers Associated With Emergence Agitation After General Anesthesia in Adult Patients: A Metabolomics Analysis

Front Med (Lausanne). 2022 Mar 23:9:828867. doi: 10.3389/fmed.2022.828867. eCollection 2022.

Authors

Xinning Mi¹, Jingshu Hong¹, Zhengqian Li¹, Taotao Liu¹, Qian Wang¹, Jiansuo Zhou², Yitong Li¹, Xiaoxiao Wang³, Yi Yuan⁴, Ning Yang¹, Yongzheng Han¹, Yang Zhou¹, Xiangyang Guo¹, Yue Li¹, Dengyang Han¹

Affiliations

¹ Department of Anesthesiology, Peking University Third Hospital, Beijing, China.
² Department of Laboratory Medicine, Peking University Third Hospital, Beijing, China.
³ Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, China.
⁴ Department of Anesthesiology, Beijing Jishuitan Hospital, Beijing, China.

Abstract

Background: Emergence agitation (EA) is a conscious disturbance after general anesthesia in adult patients that can lead to severe respiratory or circulatory complications and serious physical injury to patients and caregivers. However, the pathophysiological mechanisms underlying EA remain unclear. The present study aimed to identify serum metabolites with significant alterations in EA patients after general anesthesia and enable inferences on their associations with EA.

Methods: EA patients were identified by Richmond Agitation-Sedation Scale (RASS) ≥ + 2 among a cohort of adult patients who received elective surgery under general anesthesia in Peking University Third Hospital between 01 June 2020 and 30 December 2020. We further selected sex-, age-, and surgery type-matched non-EA control patients at a 1:1.5 ratio. Postoperative serum samples were collected from both groups of patients. An untargeted metabolic method was used to identify differences in serum metabolomic profiles between the EA patients and the non-EA patients.

Results: A total of 19 EA patients and 32 matched non-EA patients were included in the study. After screening and mapping with a database, 12 metabolites showed significant postoperative alterations in EA patients compared with non-EA patients, and were mainly involved in lipid, fatty acid and amino acid metabolism pathways. Receiver operating characteristic curve analyses indicated that vanillic acid, candoxatril, tiglylglycine, 5-methoxysalicylic acid, decanoylcarnitine, and 24-epibrassinolide may be involved in EA pathogenesis after general anesthesia.

Conclusion: In this study, we found differences in the serum levels of vanillic acid, candoxatril, tiglylglycine, 5-methoxysalicylic acid, decanoylcarnitine, and 24-epibrassinolide involved in fatty acid metabolism, lipid metabolism, and amino acid metabolism pathways in EA patients compared with non-EA patients, which may demonstrate an EA pathogenesis-associated molecular pattern and contribute toward better understanding of EA occurrence.

Keywords: decanoylcarnitine; emergence agitation; general anesthesia; pathogenesis; serum-matched metabolomics.