Population serum proteomics uncovers prognostic protein classifier and molecular mechanisms for metabolic syndrome
Cai, X.; Xue, Z.; Zeng, F.-F.; Tang, J.; Yue, L.; Wang, B.; Ge, W.; Xie, Y.; Miao, Z.; Gou, W.; Fu, Y.; Li, S.; Gao, J.; Shuai, M.; Zhang, K.; Xu, F.; Tian, Y.; Xiang, N.; Zhou, Y.; Shan, P.-F.; Zhu, Y.; Chen, Y.-m.; Zheng, J.-S.; Guo, T.
Show 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 7890 serum samples from a longitudinal cohort of 3840 participants with two follow-up time points over ten years. We then built a machine learning model for predicting the risk of developing MetS within ten years. Our model, composed of 11 proteins and the age of the individuals, achieved an area under the curve of 0.784 in the discovery cohort (n=855) and 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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