AI Article Synopsis
- - Metabolic syndrome (MetS) affects 20%-25% of the global population, and early detection could reduce the strain on healthcare systems.
- - Researchers analyzed over 400 proteins from serum samples of nearly 3,840 participants over 10 years to create a machine-learning model that predicts MetS risk within a decade.
- - The study identified key proteins linked to MetS and suggests that this large-scale proteomics research could help in developing effective prevention and treatment strategies.
Article 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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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10518601 | PMC |
| http://dx.doi.org/10.1016/j.xcrm.2023.101172 | DOI Listing |
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