Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter November 24, 2018

A study of biological and lifestyle factors, including within-subject variation, affecting concentrations of growth differentiation factor 15 in serum

  • Magdalena Krintus EMAIL logo , Federica Braga , Marek Kozinski , Simona Borille , Jacek Kubica , Grazyna Sypniewska and Mauro Panteghini

Abstract

Background

Growth differentiation factor 15 (GDF-15) is an emerging cardiovascular biomarker, and a fully automated immunoassay has recently become available. The objectives of the study were to identify biological and lifestyle factors affecting serum GDF-15 concentrations and derive robust reference intervals, and to estimate GDF-15 within-subject biological variation and derived indices.

Methods

A presumably healthy population of 533 questionnaire-screened adults was used to identify the biological and lifestyle determinants of serum GDF-15. Following stringent exclusion criteria, a final group of 173 individuals was selected to establish GDF-15 reference interval. Twenty-six healthy volunteers were enrolled in the biological variation substudy.

Results

Using a multiple regression model, age, B-type natriuretic peptide and C-reactive protein as well as smoking status were significantly related to serum GDF-15 concentrations. The upper reference limit (URL) for serum GDF-15 concentrations (90% confidence interval [CI]) was 866 ng/L (733–999 ng/L), with no sex-related difference. Although GDF-15 tended to increase with age, the weak dependence of marker from age does not justify age-related URL. The within-subject CV was 6.3% (95% CI, 4.5%–8.5%), with no sex difference in intraindividual variances. The reference change value (RCV) for GDF-15 was 23%, and two are the specimens required to ensure that the mean GDF-15 result is within ±10% of the individual’s homeostatic set point.

Conclusions

By identifying the main factors influencing serum GDF-15 concentrations, we robustly established the URL to be applied in adult population. As intraindividual variation of GDF-15 is relatively low, monitoring longitudinal changes in its concentrations over time using RCV can be a good alternative for interpreting GDF-15 in clinical setting.

Acknowledgments

GDF-15 reagents to carry out the study were a generous gift of Roche Diagnostics.

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Employment or leadership: None declared.

  4. Honorarium: None declared.

  5. Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

References

1. Wollert KC, Kempf T, Giannitsis E, Bertsch T, Braun SL, Maier H, et al. An automated assay for growth differentiation factor 15. JALM 2017;1:510–21.10.1373/jalm.2016.022376Search in Google Scholar PubMed

2. Hijazi Z, Oldgren J, Lindbäck J, Alexander JH, Connolly SJ, Eikelboom JW, et al. A biomarker-based risk score to predict death in patients with atrial fibrillation: the ABC (age, biomarkers, clinical history) death risk score. Eur Heart J 2018;39:477–85.10.1093/eurheartj/ehx584Search in Google Scholar PubMed PubMed Central

3. Wollert KC, Kempf T, Wallentin L. Growth differentiation factor 15 as a biomarker in cardiovascular disease. Clin Chem 2017;63:140–51.10.1373/clinchem.2016.255174Search in Google Scholar PubMed

4. Lind L, Wallentin L, Kempf T, Tapken H, Quint A, Lindahl B, et al. Growth-differentiation factor-15 is an independent marker of cardiovascular dysfunction and disease in the elderly: results from the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) study. Eur Heart J 2009;30:2346–53.10.1093/eurheartj/ehp261Search in Google Scholar PubMed

5. Daniels LB, Clopton P, Laughlin GA, Maisel AS, Barrett-Connor E. Growth-differentiation factor-15 is a robust, independent predictor of 11-year mortality risk in community-dwelling older adults: the Rancho Bernardo Study. Circulation 2011;123:2101–10.10.1161/CIRCULATIONAHA.110.979740Search in Google Scholar PubMed PubMed Central

6. Wang TJ, Wollert KC, Larson MG, Coglianese E, McCabe EL, Cheng S, et al. Prognostic utility of novel biomarkers of cardiovascular stress: the Framingham Heart Study. Circulation 2012;126:1596–604.10.1161/CIRCULATIONAHA.112.129437Search in Google Scholar PubMed PubMed Central

7. Andersson C, Enserro D, Sullivan L, Wang TJ, Januzzi JL Jr, Benjamin EJ, et al. Relations of circulating GDF-15, soluble ST2, and troponin-I concentrations with vascular function in the community: the Framingham Heart Study. Atherosclerosis 2016;248:245–51.10.1016/j.atherosclerosis.2016.02.013Search in Google Scholar PubMed PubMed Central

8. Rohatgi A, Patel P, Das SR, Ayers CR, Khera A, Martinez-Rumayor A, et al. Association of growth differentiation factor-15 with coronary atherosclerosis and mortality in a young, multiethnic population: observations from the Dallas Heart Study. Clin Chem 2012;58:172–82.10.1373/clinchem.2011.171926Search in Google Scholar PubMed PubMed Central

9. Hagström E, Held C, Stewart RA, Aylward PE, Budaj A, Cannon CP, et al. Growth differentiation factor 15 predicts all-cause morbidity and mortality in stable coronary heart disease. Clin Chem 2017;63:325–33.10.1373/clinchem.2016.260570Search in Google Scholar PubMed

10. Hagström E, James SK, Bertilsson M, Becker RC, Himmelmann A, Husted S, et al. Growth differentiation factor-15 level predicts major bleeding and cardiovascular events in patients with acute coronary syndromes: results from the PLATO study. Eur Heart J 2016;37:1325–33.10.1093/eurheartj/ehv491Search in Google Scholar PubMed

11. Chan MM, Santhanakrishnan R, Chong JP, Chen Z, Tai BC, Liew OW, et al. Growth differentiation factor 15 in heart failure with preserved vs. reduced ejection fraction. Eur J Heart Fail 2016;18:81–8.10.1002/ejhf.431Search in Google Scholar PubMed

12. Cotter G, Voors AA, Prescott MF, Felker GM, Filippatos G, Greenberg BH, et al. Growth differentiation factor 15 (GDF-15) in patients admitted for acute heart failure: results from the RELAX-AHF study. Eur J Heart Fail 2015;17:1133–43.10.1002/ejhf.331Search in Google Scholar PubMed

13. Wallentin L, Hijazi Z, Andersson U, Alexander JH, De Caterina R, Hanna M, et al. Growth differentiation factor 15, a marker of oxidative stress and inflammation, for risk assessment in patients with atrial fibrillation: insights from the Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation (ARISTOTLE) trial. Circulation 2014;130:1847–58.10.1161/CIRCULATIONAHA.114.011204Search in Google Scholar PubMed

14. Ho JE, Mahajan A, Chen MH, Larson MG, McCabe EL, Ghorbani A, et al. Clinical and genetic correlates of growth differentiation factor 15 in the community. Clin Chem 2012;58:1582–91.10.1373/clinchem.2012.190322Search in Google Scholar PubMed PubMed Central

15. Mueller T, Leitner I, Egger M, Haltmayer M, Dieplinger B. Association of the biomarkers soluble ST2, galectin-3 and growth-differentiation factor-15 with heart failure and other non-cardiac diseases. Clin Chim Acta 2015;445:155–60.10.1016/j.cca.2015.03.033Search in Google Scholar PubMed

16. Doerstling S, Hedberg P, Öhrvik J, Leppert J, Henriksen E. Growth differentiation factor 15 in a community-based sample: age-dependent reference limits and prognostic impact. Ups J Med Sci 2018;123:86–93.10.1080/03009734.2018.1460427Search in Google Scholar PubMed PubMed Central

17. Meijers WC, van der Velde AR, Muller Kobold AC, Dijck-Brouwer J, Wu AH, Jaffe A, et al. Variability of biomarkers in patients with chronic heart failure and healthy controls. Eur J Heart Fail 2017;19:357–65.10.1002/ejhf.669Search in Google Scholar PubMed PubMed Central

18. Braga F, Panteghini M. Generation of data on within-subject biological variation in laboratory medicine: an update. Crit Rev Clin Lab Sci 2016;53:313–25.10.3109/10408363.2016.1150252Search in Google Scholar PubMed

19. Krintus M, Kozinski M, Boudry P, Lackner K, Lefèvre G, Lennartz L, et al. Defining normality in a European multinational cohort: critical factors influencing the 99th percentile upper reference limit for high sensitivity cardiac troponin I. Int J Cardiol 2015;187:256–63.10.1016/j.ijcard.2015.03.282Search in Google Scholar PubMed

20. Krintus M, Kozinski M, Fabiszak T, Kubica J, Panteghini M, Sypniewska G. Establishing reference intervals for galectin-3 concentrations in serum requires careful consideration of its biological determinants. Clin Biochem 2017;50:599–604.10.1016/j.clinbiochem.2017.03.015Search in Google Scholar PubMed

21. Krintus M, Kozinski M, Braga F, Kubica J, Sypniewska G, Panteghini M. Plasma midregional proadrenomedullin (MR-proADM) concentrations and their biological determinants in a reference population. Clin Chem Lab Med 2018;56:1161–8.10.1515/cclm-2017-1044Search in Google Scholar PubMed

22. Krintus M, Kozinski M, Boudry P, Capell NE, Köller U, Lackner K, et al. European multicenter analytical evaluation of the Abbott ARCHITECT STAT high sensitive troponin I immunoassay. Clin Chem Lab Med 2014;52:1657–65.10.1515/cclm-2014-0107Search in Google Scholar PubMed

23. Ponikowski P, Voors AA, Anker SD, Bueno H, Cleland JG, Coats AJ, et al. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: the task force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur Heart J 2016;37:2129–200.10.1002/ejhf.592Search in Google Scholar PubMed

24. Pearson TA, Mensah GA, Alexander RW, Anderson JL, Cannon RO 3rd, et al. Centers for Disease Control and Prevention; American Heart Association. 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;107:499–511.10.1161/01.CIR.0000052939.59093.45Search in Google Scholar PubMed

25. Panteghini M, John WG. Implementation of haemoglobin A1c results traceable to the IFCC reference system: the way forward. Clin Chem Lab Med 2007;45:942–4.10.1515/CCLM.2007.198Search in Google Scholar PubMed

26. Stevens PE, Levin A. Evaluation and management of chronic kidney disease: synopsis of the Kidney Disease: Improving Global Outcomes 2012 clinical practice guideline. Ann Intern Med 2013;158:825–30.10.7326/0003-4819-158-11-201306040-00007Search in Google Scholar PubMed

27. Roche Cobas Elecsys GDF-15 package insert, 03.2018, V.2.0.Search in Google Scholar

28. CLSI. Defining, establishing and verifying reference intervals in the clinical laboratory; Approved guideline. CLSI document EP28A3c. Wayne PA. Clinical and Laboratory Standards Institute, 2010.Search in Google Scholar

29. Reed AH, Henry RJ, Mason WB. Influence of statistical method used on the resulting estimate of normal range. Clin Chem 1971;17:275–84.10.1093/clinchem/17.4.275Search in Google Scholar

30. Røraas T, Støve B, Petersen PH, Sandberg S. Biological variation: evaluation of methods for constructing confidence intervals for estimates of within-person biological variation for different distributions of the within-person effect. Clin Chim Acta 2017;468:166–73.10.1016/j.cca.2017.02.021Search in Google Scholar PubMed

31. Røraas T, Støve B, Petersen PH, Sandberg S. Biological variation: the effect of different distributions on estimated within-person variation and reference change values. Clin Chem 2016;62: 725–36.10.1373/clinchem.2015.252296Search in Google Scholar PubMed

32. Braga F, Ferraro S, Mozzi R, Panteghini M. The importance of individual biology in the clinical use of serum biomarkers for ovarian cancer. Clin Chem Lab Med 2014;52:1625–31.10.1515/cclm-2014-0097Search in Google Scholar PubMed

33. Pasqualetti S, Infusino I, Carnevale A, Szoke D, Panteghini M. The calibrator value assignment protocol of the Abbott enzymatic creatinine assay is inadequate for ensuring suitable quality of serum measurements. Clin Chim Acta 2015;450:125–6.10.1016/j.cca.2015.08.007Search in Google Scholar PubMed

34. Røraas T, Petersen PH, Sandberg S. Confidence intervals and power calculations for within-person biological variation: effect of analytical imprecision, number of replicates, number of samples, and number of individuals. Clin Chem 2012;58:1306–13.10.1373/clinchem.2012.187781Search in Google Scholar PubMed

35. Fraser CG, Petersen PH. The importance of imprecision. Ann Clin Biochem 1991;28:207–11.10.1177/000456329102800301Search in Google Scholar PubMed

36. Fraser CG, Hyltoft Peterson P, Libeer JC, Ricos C. Proposal for setting generally applicable quality goals solely based on biology. Ann Clin Biochem 1997;34:8–12.10.1177/000456329703400103Search in Google Scholar PubMed

37. Sandberg S, Fraser CG, Horvath AR, Jansen R, Jones G, Oosterhuis W, et al. Defining analytical performance specifications: consensus statement from the 1st Strategic Conference of the European Federation of Clinical Chemistry and Laboratory Medicine. Clin Chem Lab Med 2015;53:833–5.10.1515/cclm-2015-0067Search in Google Scholar PubMed

38. Ceriotti F, Hinzmann R, Panteghini M. Reference intervals: the way forward. Ann Clin Biochem 2009;46:8–17.10.1258/acb.2008.008170Search in Google Scholar PubMed

39. Panteghini M, Adeli K, Ceriotti F, Sandberg S, Horvath AR. American liver guidelines and cutoffs for “normal” ALT: a potential for overdiagnosis. Clin Chem 2017;63:1196–8.10.1373/clinchem.2017.274977Search in Google Scholar PubMed

40. Kempf T, Horn-Wichmann R, Brabant G, Peter T, Allhoff T, Klein G, et al. Circulating concentrations of growth differentiation factor 15 in apparently healthy elderly individuals and patients with chronic heart failure as assessed by a new immunoradiometric sandwich assay. Clin Chem 2007;53:284–91.10.1373/clinchem.2006.076828Search in Google Scholar PubMed

41. Ceriotti F. Quality specifications for the extra-analytical phase of laboratory testing: reference intervals and decision limits. Clin Biochem 2017;50:595–8.10.1016/j.clinbiochem.2017.03.024Search in Google Scholar PubMed

42. Aarsand AK, Røraas T, Fernandez-Calle P, Ricos C, Díaz-Garzón J, Jonker N, et al. The Biological Variation Data Critical Appraisal Checklist: a standard for evaluating studies on biological variation. Clin Chem 2018;64:501–14.10.1373/clinchem.2017.281808Search in Google Scholar PubMed

Received: 2018-08-22
Accepted: 2018-10-18
Published Online: 2018-11-24
Published in Print: 2019-06-26

©2018 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 28.3.2024 from https://www.degruyter.com/document/doi/10.1515/cclm-2018-0908/html
Scroll to top button