Abstract
The biomarker glycoprotein acetylation (GlycA) hasbeen shown to predict risk of cardiovascular disease and all-cause mortality. Here, we characterize biological processes associated with GlycA by leveraging population-based omics data and health records from >10,000 individuals. Our analyses show that GlycA levels are chronic within individuals for up to a decade. In apparently healthy individuals, elevated GlycA corresponded to elevation of myriad inflammatory cytokines, as well as a gene coexpression network indicative of increased neutrophil activity, suggesting that individuals with high GlycA may be in a state of chronic inflammatory response. Accordingly, analysis of infection-related hospitalization and death records showed that increased GlycA increased long-term risk of severe non-localized and respiratory infections, particularly septicaemia and pneumonia. In total, our work demonstrates that GlycA is a biomarker for chronic inflammation, neutrophil activity, and risk of future severe infection.It also illustrates the utility of leveraging multi-layeredomics data and health records to elucidate the molecular and cellular processes associated with biomarkers.
Highlights
The integration of large-scale, systems-wide biomolecular information with health records to identify statistical associations represents a foundation of future studies in ‘‘precision medicine.’’ Early systems-wide studies have elucidated the etiology of complex diseases in natural human populations (Chen et al, 2012, 2008; Emilsson et al, 2008; Montoya et al, 2014; Stanberry et al, 2013; Wang et al, 2011; Zhang et al, 2013)
The Dietary, Lifestyle, and Genetic Determinants of Obesity and Metabolic syndrome (DILGOM) Study is a cross-sectional cohort of 579 individuals (300 female; 52%) 25–74 years old (Inouye et al, 2010a, 2010b)
We observed modest elevation of glycoprotein acetylation (GlycA) in individuals from all Young Finns Study (YFS) collections (mean increase of 0.41 SD; A Transcriptional Subnetwork Associated with GlycA When put in the context of previous studies, our results suggest that, in addition to marking the acute-phase response (Bell et al, 1987), high GlycA levels may mark chronic inflammation in apparently healthy individuals
Summary
The integration of large-scale, systems-wide biomolecular information with health records to identify statistical associations represents a foundation of future studies in ‘‘precision medicine.’’ Early systems-wide studies have elucidated the etiology of complex diseases in natural human populations (Chen et al, 2012, 2008; Emilsson et al, 2008; Montoya et al, 2014; Stanberry et al, 2013; Wang et al, 2011; Zhang et al, 2013). In doing so, they have identified biomarkers of potential clinical utility.
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