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Si-Wen Gui,1,2,* Yi-Yun Liu,1–3,* Xiao-Gang Zhong,1,2,4,* Xinyu Liu,5,* Peng Zheng,1–3 Jun-Cai Pu,1–3 Jian Zhou,1,2 Jian-Jun Chen,1,2 Li-Bo Zhao,6 Lan-Xiang Liu,1–3 Guowang Xu,5 Peng Xie1–3 1Chongqing Key Laboratory of Neurobiology, Chongqing, China; 2Institute of Neuroscience and the Collaborative Innovation Center for Brain Science, Chongqing Medical University, Chongqing, China; 3Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China; 4School of Public Health and Management, Chongqing Medical University, Chongqing, China; 5CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China; 6Department of Neurology, Yongchuan Hospital of Chongqing Medical University, Chongqing, China *These authors contributed equally to this work Introduction: Major depressive disorder (MDD) is a highly prevalent mental disorder affecting millions of people worldwide. However, a clear causative etiology of MDD remains unknown. In this study, we aimed to identify critical protein alterations in plasma from patients with MDD and integrate our proteomics and previous metabolomics data to reveal significantly perturbed pathways in MDD. An isobaric tag for relative and absolute quantification (iTRAQ)-based quantitative proteomics approach was conducted to compare plasma protein expression between patients with depression and healthy controls (CON). Methods: For integrative analysis, Ingenuity Pathway Analysis software was used to analyze proteomics and metabolomics data and identify potential relationships among the differential proteins and metabolites. Results: A total of 74 proteins were significantly changed in patients with depression compared with those in healthy CON. Bioinformatics analysis of differential proteins revealed significant alterations in lipid transport and metabolic function, including