Identication of Novel Biomarkers for Metabolic Syndrome Based on Machine Learning Algorithms and Integrated Bioinformatics Analysis Guanzhi Liu Xi'an Jiaotong University Second Aliated Hospital https://orcid.org/0000-0003-1626-5006 Chen Chen Department of Cardiovascular Medicine, First Aliated Hospital of Xi’an Jiaotong University, Xi’an, China Ning Kong Bone and Joint Surgery Center, Second Aliated Hospital of Xi’an Jiaotong University, Xi’an, China Yutian Lei Bone and Joint Surgery Center, Second Aliated Hospital of Xi’an Jiaotong University, Xi’an, China Sen Luo Bone and Joint Surgery Center, Second Aliated Hospital of Xi’an Jiaotong University, Xi’an, China Zhuo Huang Bone and Joint Surgery Center, Second Aliated Hospital of Xi’an Jiaotong University, Xi’an, China Kunzheng Wang Bone and Joint Surgery Center, Second Aliated Hospital of Xi’an Jiaotong University, Xi’an, China Pei Yang Bone and Joint Surgery Center, Second Aliated Hospital of Xi’an Jiaotong University, Xi’an, China Xin Huang (
[email protected] ) Department of Cardiovascular Medicine, First Aliated Hospital of Xi’an Jiaotong University, Xi’an, China Research Keywords: metabolic syndrome, WGCNA, diagnostic biomarkers, bioinformatics, machine learning Posted Date: February 24th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-225591/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/20 Abstract Background: Metabolic syndrome is a common and complicated metabolic disorder and dened as a clustering of metabolic risk factors such as insulin resistance or diabetes, obesity, hypertension, and hyperlipidemia. However, its early diagnosis is limited because the lack of denitive clinical diagnostic biomarkers.