Population Serum Proteomics Uncovers a Prognostic Protein Classifier for Metabolic Syndrome

Overview

Journal Cell Rep Med

Publisher Cell Press

Specialties Biology
General Medicine

Date 2023 Aug 31

PMID 37652016

Authors

Xue Cai

Zhangzhi Xue

Fang-Fang Zeng

Jun Tang

Liang Yue

Bo Wang

Weigang Ge

Yuting Xie

Zelei Miao

Wanglong Gou

Yuanqing Fu

Sainan Li

Jinlong Gao

Menglei Shuai

Ke Zhang

Fengzhe Xu

Yunyi Tian

Nan Xiang

Yan Zhou

Peng-Fei Shan

Yi Zhu

Yu-Ming Chen

Ju-Sheng Zheng

Tiannan Guo

Affiliations

Soon will be listed here.

Abstract

Metabolic syndrome (MetS) is a complex metabolic disorder with a global prevalence of 20%-25%. Early identification and intervention would help minimize the global burden on healthcare systems. Here, we measured over 400 proteins from ∼20,000 proteomes using data-independent acquisition mass spectrometry for 7,890 serum samples from a longitudinal cohort of 3,840 participants with two follow-up time points over 10 years. We then built a machine-learning model for predicting the risk of developing MetS within 10 years. Our model, composed of 11 proteins and the age of the individuals, achieved an area under the curve of 0.774 in the validation cohort (n = 242). Using linear mixed models, we found that apolipoproteins, immune-related proteins, and coagulation-related proteins best correlated with MetS development. This population-scale proteomics study broadens our understanding of MetS and may guide the development of prevention and targeted therapies for MetS.

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