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
doi: 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:

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

Zdroje

1. Auffray C, Chen Z, Hood L. Systems medicine: the future of medical genomics and healthcare. Genome Med. 2009;1(1): 2. doi: 10.1186/gm2 19348689

2. Vasan RS. Biomarkers of cardiovascular disease: molecular basis and practical considerations. Circulation. 2006;113(19): 2335–2362. doi: 10.1161/CIRCULATIONAHA.104.482570 16702488

3. Connelly MA, Otvos JD, Shalaurova I, Playford MP, Mehta NN. GlycA, a novel biomarker of systemic inflammation and cardiovascular disease risk. J Transl Med. 2017 Oct 27;15(1): 219. doi: 10.1186/s12967-017-1321-6 29078787

4. Ala-Korpela M. Serum nuclear magnetic resonance spectroscopy: one more step toward clinical utility. Clin Chem. 2015;61(5): 681–683. doi: 10.1373/clinchem.2015.238279 25759464

5. Kettunen J, Ritchie SC, Anufrieva O, Lyytikäinen L-P, Hernesniemi J, Karhunen PJ, et al. Biomarker Glycoprotein Acetyls Is Associated With the Risk of a Wide Spectrum of Incident Diseases and Stratifies Mortality Risk in Angiography Patients. Circ Genomic Precis Med. 2018 Nov;11(11): e002234.

6. Akinkuolie AO, Buring JE, Ridker PM, Mora S. A novel protein glycan biomarker and future cardiovascular disease events. J Am Heart Assoc. 2014 Oct;3(5): e001221. doi: 10.1161/JAHA.114.001221 25249300

7. Gruppen EG, Riphagen IJ, Connelly MA, Otvos JD, Bakker SJL, Dullaart RPF. GlycA, a Pro-Inflammatory Glycoprotein Biomarker, and Incident Cardiovascular Disease: Relationship with C-Reactive Protein and Renal Function. PLoS One. 2015;10(9): e0139057. doi: 10.1371/journal.pone.0139057 26398105

8. Duprez DA, Otvos J, Sanchez OA, Mackey RH, Tracy R, Jacobs DR Jr. Comparison of the Predictive Value of GlycA and Other Biomarkers of Inflammation for Total Death, Incident Cardiovascular Events, Noncardiovascular and Noncancer Inflammatory-Related Events, and Total Cancer Events. Clin Chem. 2016 Jul;62(7): 1020–1031. doi: 10.1373/clinchem.2016.255828 27173011

9. Chandler PD, Akinkuolie AO, Tobias DK, Lawler PR, Li C, Moorthy MV, et al. Association of N-Linked Glycoprotein Acetyls and Colorectal Cancer Incidence and Mortality. PLoS One. 2016;11(11): e0165615. doi: 10.1371/journal.pone.0165615 27902713

10. Fischer K, Kettunen J, Würtz P, Haller T, Havulinna AS, Kangas AJ, et al. Biomarker profiling by nuclear magnetic resonance spectroscopy for the prediction of all-cause mortality: an observational study of 17,345 persons. PLoS Med. 2014 Feb;11(2): e1001606. doi: 10.1371/journal.pmed.1001606 24586121

11. Akinkuolie AO, Pradhan AD, Buring JE, Ridker PM, Mora S. Novel protein glycan side-chain biomarker and risk of incident type 2 diabetes mellitus. Arterioscler Thromb Vasc Biol. 2015 Jun;35(6): 1544–1550. doi: 10.1161/ATVBAHA.115.305635 25908766

12. Connelly MA, Gruppen EG, Wolak-Dinsmore J, Matyus SP, Riphagen IJ, Shalaurova I, et al. GlycA, a marker of acute phase glycoproteins, and the risk of incident type 2 diabetes mellitus: PREVEND study. Clin Chim Acta. 2016;452: 10–17. doi: 10.1016/j.cca.2015.11.001 26549655

13. Fizelova M, Jauhiainen R, Kangas AJ, Soininen P, Ala-Korpela M, Kuusisto J, et al. Differential Associations of Inflammatory Markers With Insulin Sensitivity and Secretion: The Prospective METSIM Study. J Clin Endocrinol Metab. 2017 Sep 1;102(9): 3600–3609. doi: 10.1210/jc.2017-01057 28911155

14. Kaikkonen JE, Würtz P, Suomela E, Lehtovirta M, Kangas AJ, Jula A, et al. Metabolic profiling of fatty liver in young and middle-aged adults: cross-sectional and prospective analyses of the Young Finns Study. Hepatology. 2016;65: 491–500. doi: 10.1002/hep.28899 27775848

15. Ritchie SC, Würtz P, Nath AP, Abraham G, Havulinna AS, Fearnley LG, et al. The Biomarker GlycA is Associated with Chronic Inflammation and Predicts Long-Term Risk of Severe Infection. Cell Syst. 2015 Oct;1(4): 293–301. doi: 10.1016/j.cels.2015.09.007 27136058

16. Bell JD, Brown JC, Nicholson JK, Sadler PJ. Assignment of resonances for “acute-phase” glycoproteins in high resolution proton NMR spectra of human blood plasma. FEBS Lett. 1987;215(2): 311–315. doi: 10.1016/0014-5793(87)80168-0 2438159

17. Otvos JD, Shalaurova I, Wolak-Dinsmore J, Connelly MA, Mackey RH, Stein JH, et al. GlycA: A composite nuclear magnetic resonance biomarker of systemic inflammation. Clin Chem. 2015;61(5): 714–723. doi: 10.1373/clinchem.2014.232918 25779987

18. Lauridsen MB, Bliddal H, Christensen R, Danneskiold-Samsøe B, Bennett R, Keun H, et al. 1H NMR spectroscopy-based interventional metabolic phenotyping: A cohort study of rheumatoid arthritis patients. J Proteome Res. 2010;9(9): 4545–4553. doi: 10.1021/pr1002774 20701312

19. Bartlett DB, Connelly MA, AbouAssi H, Bateman LA, Tune KN, Huebner JL, et al. A novel inflammatory biomarker, GlycA, associates with disease activity in rheumatoid arthritis and cardio-metabolic risk in BMI-matched controls. Arthritis Res Ther. 2016;18: 86. doi: 10.1186/s13075-016-0982-5 27067270

20. Chung CP, Ormseth MJ, Connelly MA, Oeser A, Solus JF, Otvos JD, et al. GlycA, a novel marker of inflammation, is elevated in systemic lupus erythematosus. Lupus. 2016 Mar;25(3): 296–300. doi: 10.1177/0961203315617842 26637290

21. Gruppen EG, Connelly MA, Otvos JD, Bakker SJL, Dullaart RPF. A novel protein glycan biomarker and LCAT activity in metabolic syndrome. Eur J Clin Invest. 2015 Aug;45(8): 850–859. doi: 10.1111/eci.12481 26081900

22. Pearson TA, Mensah GA, Alexander RW, Anderson JL, Cannon RO 3rd, Criqui M, et al. Markers of inflammation and cardiovascular disease: application to clinical and public health practice: A statement for healthcare professionals from the Centers for Disease Control and Prevention and the American Heart Association. Circulation. 2003 Jan;107(3): 499–511. doi: 10.1161/01.cir.0000052939.59093.45 12551878

23. Gabay C, Kushner I. Acute-phase proteins and other systemic responses to inflammation. N Engl J Med. 1999;340(6): 448–454. doi: 10.1056/NEJM199902113400607 9971870

24. Konttinen H, Männistö S, Sarlio-Lähteenkorva S, Silventoinen K, Haukkala A. Emotional eating, depressive symptoms and self-reported food consumption. A population-based study. Appetite. 2010 Jun;54(3): 473–479. doi: 10.1016/j.appet.2010.01.014 20138944

25. Tibshirani R. Regression shrinkage and selection via the Lasso. J R Stat Soc Series B Stat Methodol. 1996;58(1): 267–288.

26. Hastie T, Tibshirani R, Friedman JH. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Science & Business Media; 2001.

27. Vartiainen E, Laatikainen T, Peltonen M, Juolevi A, Männistö S, Sundvall J, et al. Thirty-five-year trends in cardiovascular risk factors in Finland. Int J Epidemiol. 2010 Apr;39(2): 504–518. doi: 10.1093/ije/dyp330 19959603

28. Borodulin K, Vartiainen E, Peltonen M, Jousilahti P, Juolevi A, Laatikainen T, et al. Forty-year trends in cardiovascular risk factors in Finland. Eur J Public Health. 2015;25(3): 539–546. doi: 10.1093/eurpub/cku174 25422363

29. Würtz P, Havulinna AS, Soininen P, Tynkkynen T, Prieto-Merino D, Tillin T, et al. Metabolite profiling and cardiovascular event risk: a prospective study of 3 population-based cohorts. Circulation. 2015 Mar 3;131(9): 774–785. doi: 10.1161/CIRCULATIONAHA.114.013116 25573147

30. Mootha VK, Lindgren CM, Eriksson K-F, Subramanian A, Sihag S, Lehar J, et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003 Jul;34(3): 267–273. doi: 10.1038/ng1180 12808457

31. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43): 15545–15550. doi: 10.1073/pnas.0506580102 16199517

32. Inouye M, Silander K, Hamalainen E, Salomaa V, Harald K, Jousilahti P, et al. An immune response network associated with blood lipid levels. PLoS Genet. 2010 Sep;6(9): e1001113. doi: 10.1371/journal.pgen.1001113 20844574

33. Inouye M, Kettunen J, Soininen P, Silander K, Ripatti S, Kumpula LS, et al. Metabonomic, transcriptomic, and genomic variation of a population cohort. Mol Syst Biol. 2010;6: 441. doi: 10.1038/msb.2010.93 21179014

34. Nath AP, Ritchie SC, Byars SG, Fearnley LG, Havulinna AS, Joensuu A, et al. An interaction map of circulating metabolites, immune gene networks, and their genetic regulation. Genome Biol. 2017;18(1). doi: 10.1186/s13059-017-1279-y 28764798

35. Li S, Rouphael N, Duraisingham S, Romero-Steiner S, Presnell S, Davis C, et al. Molecular signatures of antibody responses derived from a systems biology study of five human vaccines. Nat Immunol. 2014 Feb;15(2): 195–204. doi: 10.1038/ni.2789 24336226

36. Kalsheker N. Alpha1-antitrypsin: Structure, function and molecular biology of the gene. Biosci Rep. 1989;9: 129–138. doi: 10.1007/bf01115992 2669992

37. DeMeo DL, Silverman EK. Alpha1-antitrypsin deficiency. 2: genetic aspects of alpha(1)-antitrypsin deficiency: phenotypes and genetic modifiers of emphysema risk. Thorax. 2004 Mar;59(3): 259–264. doi: 10.1136/thx.2003.006502 14985567

38. Stoller JK, Aboussouan LS. A review of α1-antitrypsin deficiency. Am J Respir Crit Care Med. 2012 Feb 1;185(3): 246–259. doi: 10.1164/rccm.201108-1428CI 21960536

39. Laurell C-B, Eriksson S. The electrophoretic α; 1-globulin pattern of serum in α; 1-antitrypsin deficiency. Scand J Clin Lab Invest. 1963;15(2): 132–140.

40. Wilson Cox D, Huber O. Rheumatoid Arthritis and Alpha-1-antitrypsin. Lancet. 1976;307(7971): 1216–1217.

41. Hashemi M, Naderi M, Rashidi H, Ghavami S. Impaired activity of serum alpha-1-antitrypsin in diabetes mellitus. Diabetes Res Clin Pract. 2007 Feb;75(2): 246–248. doi: 10.1016/j.diabres.2006.06.020 16875754

42. Dickson I, Alper CA. Changes in serum proteinase inhibitor levels following bone surgery. Clin Chim Acta. 1974;54(3): 381–385. doi: 10.1016/0009-8981(74)90257-5 4547155

43. Ehlers MR. Immune-modulating effects of alpha-1 antitrypsin. Biol Chem. 2014 Oct;395(10): 1187–1193. doi: 10.1515/hsz-2014-0161 24854541

44. Tabas I, Glass CK. Anti-inflammatory therapy in chronic disease: challenges and opportunities. Science (80-). 2013 Jan 11;339(6116): 166–172.

45. IL6R Genetics Consortium Emerging Risk Factors Collaboration, Sarwar N, Butterworth AS, Freitag DF, Gregson J, Willeit P, et al. Interleukin-6 receptor pathways in coronary heart disease: a collaborative meta-analysis of 82 studies. Lancet. 2012 Mar;379(9822): 1205–1213. doi: 10.1016/S0140-6736(11)61931-4 22421339

46. Ferreira RC, Freitag DF, Cutler AJ, Howson JMM, Rainbow DB, Smyth DJ, et al. Functional IL6R 358Ala allele impairs classical IL-6 receptor signaling and influences risk of diverse inflammatory diseases. PLoS Genet. 2013 Apr;9(4): e1003444. doi: 10.1371/journal.pgen.1003444 23593036

47. Ridker PM, Everett BM, Thuren T, MacFadyen JG, Chang WH, Ballantyne C, et al. Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease. N Engl J Med. 2017 Aug;337: 1119–1131.

48. Ridker PM, MacFadyen JG, Thuren T, Everett BM, Libby P, Glynn RJ, et al. Effect of interleukin-1β inhibition with canakinumab on incident lung cancer in patients with atherosclerosis: exploratory results from a randomised, double-blind, placebo-controlled trial. Lancet. 2017 Aug;390: 1833–1842. doi: 10.1016/S0140-6736(17)32247-X 28855077

49. Lewis EC. Expanding the clinical indications for α(1)-antitrypsin therapy. Mol Med. 2012 Sep 7;18: 957–970. doi: 10.2119/molmed.2011.00196 22634722

50. Setoh K, Terao C, Muro S, Kawaguchi T, Tabara Y, Takahashi M, et al. Three missense variants of metabolic syndrome-related genes are associated with alpha-1 antitrypsin levels. Nat Commun. 2015 Jul 15;6: 7754. doi: 10.1038/ncomms8754 26174136

51. Suhre K, Arnold M, Bhagwat AM, Cotton RJ, Engelke R, Raffler J, et al. Connecting genetic risk to disease end points through the human blood plasma proteome. Nat Commun. 2017 Feb 27;8: 14357. doi: 10.1038/ncomms14357 28240269

52. Würtz P, Kangas AJ, Soininen P, Lawlor DA, Davey Smith G, Ala-Korpela M. Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technology. Am J Epidemiol. 2017 Sep;186: 1084–1096. doi: 10.1093/aje/kwx016 29106475

53. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33(1): 1–22. 20808728

54. Viechtbauer W. Conducting Meta-Analyses in R with the metafor Package. J Stat Softw. 2010;36(3): 1–48.

55. Storey JD. A direct approach to false discovery rates. J R Stat Soc Series B Stat Methodol. 2002;64(3): 479–498.

56. Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 2015;1(6):417–25. doi: 10.1016/j.cels.2015.12.004 26771021

57. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000 May;25(1): 25–29. doi: 10.1038/75556 10802651

58. Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res. 2015 Jan;43(Database issue): D1049–D1056. doi: 10.1093/nar/gku1179 25428369

59. Kanehisa M, Goto S. Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000;28(1): 27–30. doi: 10.1093/nar/28.1.27 10592173

60. Croft D, Mundo AF, Haw R, Milacic M, Weiser J, Wu G, et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 2014 Jan;42(Database issue): D472–D477. doi: 10.1093/nar/gkt1102 24243840

61. Ritchie SC, Watts S, Fearnley LG, Holt KE, Abraham G, Inouye M. A Scalable Permutation Approach Reveals Replication and Preservation Patterns of Network Modules in Large Datasets. Cell Syst. 2016 Jul;3(1): 71–82. doi: 10.1016/j.cels.2016.06.012 27467248

62. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008 Jan;9: 559. doi: 10.1186/1471-2105-9-559 19114008

63. Eden E, Navon R, Steinfeld I, Lipson D, Yakhini Z. GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics. 2009;10: 48. doi: 10.1186/1471-2105-10-48 19192299

64. Supek F, Bošnjak M, Škunca N, Šmuc T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One. 2011;6(7): e21800. doi: 10.1371/journal.pone.0021800 21789182

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

Souhrn

Background

Methods

Results

Conclusions

Klíčová slova:

Zdroje

PLOS One

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