Elevated serum alpha-1 antitrypsin is a major component of GlycA-associated risk for future morbidity and mortality

English version

Autoři: Scott C. Ritchie ^aff001; Johannes Kettunen ^aff004; Marta Brozynska ^aff001; Artika P. Nath ^aff001; Aki S. Havulinna ^aff005; Satu Männistö ^aff005; Markus Perola ^aff005; Veikko Salomaa ^aff005; Mika Ala-Korpela ^aff004; Gad Abraham ^aff001; Peter Würtz ^aff013; Michael Inouye ^aff001
Působiště autorů: Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia ^aff001; Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom ^aff002; Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia ^aff003; Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland ^aff004; National Institute for Health and Welfare, Helsinki, Finland ^aff005; NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland ^aff006; Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland ^aff007; Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom ^aff008; Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom ^aff009; Systems Epidemiology Lab, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia ^aff010; Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, The Alfred Hospital, Monash University, Melbourne, Victoria, Australia ^aff011; School of BioSciences, The University of Melbourne, Parkville, Victoria, Australia ^aff012; Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland ^aff013; Nightingale Health Ltd, Helsinki, Finland ^aff014; The Alan Turing Institute, London, United Kingdom ^aff015
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0223692

Souhrn

Background

GlycA is a nuclear magnetic resonance (NMR) spectroscopy biomarker that predicts risk of disease from myriad causes. It is heterogeneous; arising from five circulating glycoproteins with dynamic concentrations: alpha-1 antitrypsin (AAT), alpha-1-acid glycoprotein (AGP), haptoglobin (HP), transferrin (TF), and alpha-1-antichymotrypsin (AACT). The contributions of each glycoprotein to the disease and mortality risks predicted by GlycA remain unknown.

Methods

We trained imputation models for AAT, AGP, HP, and TF from NMR metabolite measurements in 626 adults from a population cohort with matched NMR and immunoassay data. Levels of AAT, AGP, and HP were estimated in 11,861 adults from two population cohorts with eight years of follow-up, then each biomarker was tested for association with all common endpoints. Whole blood gene expression data was used to identify cellular processes associated with elevated AAT.

Results

Accurate imputation models were obtained for AAT, AGP, and HP but not for TF. While AGP had the strongest correlation with GlycA, our analysis revealed variation in imputed AAT levels was the most predictive of morbidity and mortality for the widest range of diseases over the eight year follow-up period, including heart failure (meta-analysis hazard ratio = 1.60 per standard deviation increase of AAT, P-value = 1×10⁻¹⁰), influenza and pneumonia (HR = 1.37, P = 6×10⁻¹⁰), and liver diseases (HR = 1.81, P = 1×10⁻⁶). Transcriptional analyses revealed association of elevated AAT with diverse inflammatory immune pathways.

Conclusions

This study clarifies the molecular underpinnings of the GlycA biomarker’s associated disease risk, and indicates a previously unrecognised association between elevated AAT and severe disease onset and mortality.

Klíčová slova:

Gene expression – Blood – Immunoassays – Biomarkers – Glycoproteins – Inflammatory diseases – Gene ontologies

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