The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.
Growth Differentiation Factor 15 as a Biomarker in Cardiovascular Disease
Abstract
BACKGROUND: Growth differentiation factor 15 (GDF-15) is expressed and secreted in response to inflammation, oxidative stress, hypoxia, telomere erosion, and oncogene activation. Cardiovascular (CV) disease is a major driver of GDF-15 production. GDF-15 has favorable preanalytic characteristics and can be measured in serum and plasma by immunoassay. CONTENT: In community-dwelling individuals higher concentrations of GDF-15 are associated with increased risks of developing CV disease, chronic kidney disease, and cancer, independent of traditional CV risk factors, renal function, and other biomarkers (C-reactive protein, B-type natriuretic peptide, cardiac troponin). Low concentrations of GDF-15 are closely associated with longevity. GDF-15 is as an independent marker of all-cause mortality and CV events in patients with coronary artery disease, and may help select patients with non–ST-elevation acute coronary syndrome for early revascularization and more intensive medical therapies. GDF-15 is independently associated with mortality and nonfatal events in atrial fibrillation and heart failure (HF) with preserved or reduced ejection fraction. GDF-15 reflects chronic disease burden and acute perturbations in HF and responds to improvements in hemodynamic status. GDF-15 is independently associated with major bleeding in patients receiving antithrombotic therapies and has been included in a new bleeding risk score, which may become useful for decision support. SUMMARY: GDF-15 captures distinct aspects of CV disease development, progression, and prognosis, which are not represented by clinical risk predictors and other biomarkers. The usefulness of GDF-15 to guide management decisions and discover new treatment targets should be further explored.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A Novel Paradigm for Heart Failure With Preserved Ejection Fraction
Paulus, W. J., & Tschöpe, C. (2013) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2013.02.092 · Cited by 3,564
C-Reactive Protein Levels and Outcomes after Statin Therapy
Ridker, P. M., Cannon, C. P., Morrow, D., Rifai, N., Rose, L. M., McCabe, C. H., Pfeffer, M. A., & Braunwald, E. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa042378 · Cited by 2,292
Vousden, K. H., & Lane, D. P. (2007) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2147 · Cited by 2,272
Shared Risk Factors in Cardiovascular Disease and Cancer
Koene, R. J., Prizment, A. E., Blaes, A., & Konety, S. H. (2016) · Circulation · doi:10.1161/circulationaha.115.020406 · Cited by 1,494
MIC-1, a novel macrophage inhibitory cytokine, is a divergent member of the TGF-β superfamily
Bootcov, M. R., Bauskin, A. R., Valenzuela, S. M., Moore, A. G., Bansal, M., He, X. Y., Zhang, H. P., Donnellan, M., Mahler, S., Pryor, K., et al. (1997) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.94.21.11514 · Cited by 1,177
Hlatky, M. A., Greenland, P., Arnett, D. K., Ballantyne, C. M., Criqui, M. H., Elkind, M. S. V., Go, A. S., Harrell, F. E., Hong, Y., Howard, B. V., et al. (2009) · Circulation · doi:10.1161/circulationaha.109.192278 · Cited by 1,139
Rothwell, P. M., Price, J. F., Fowkes, F. G. R., Zanchetti, A., Roncaglioni, M. C., Tognoni, G., Lee, R., Belch, J. F., Wilson, M., Mehta, Z., et al. (2012) · The Lancet · doi:10.1016/s0140-6736(11)61720-0 · Cited by 839
Kempf, T., Eden, M., Strelau, J., Naguib, M., Willenbockel, C., Tongers, J., Heineke, J., Kotlarz, D., Xu, J., Molkentin, J. D., et al. (2006) · Circulation Research · doi:10.1161/01.res.0000202805.73038.48 · Cited by 651
Self-citationKempf, T., Zarbock, A., Widera, C., Butz, S., Stadtmann, A., Rossaint, J., Bolomini-Vittori, M., Korf-Klingebiel, M., Napp, L. C., Hansen, B., et al. (2011) · Nature Medicine · doi:10.1038/nm.2354 · Cited by 518
Xu, J., Kimball, T. R., Lorenz, J. N., Brown, D. A., Bauskin, A. R., Klevitsky, R., Hewett, T. E., Breit, S. N., & Molkentin, J. D. (2006) · Circulation Research · doi:10.1161/01.res.0000202804.84885.d0 · Cited by 506
Cited by
A proteomic atlas of senescence-associated secretomes for aging biomarker development
Basisty, N., Kale, A., Jeon, O. H., Kuehnemann, C., Payne, T., Rao, C., Holtz, A., Shah, S., Sharma, V., Ferrucci, L., et al. (2020) · PLOS Biology · doi:10.1371/journal.pbio.3000599 · Cited by 1,348
GDF15 Provides an Endocrine Signal of Nutritional Stress in Mice and Humans
Patel, S., Alvarez-Guaita, A., Melvin, A., Rimmington, D., Dattilo, A., Miedzybrodzka, E. L., Cimino, I., Maurin, A.-C., Roberts, G. P., Meek, C. L., et al. (2019) · Cell Metabolism · doi:10.1016/j.cmet.2018.12.016 · Cited by 509
A Longitudinal Big Data Approach for Precision Health
Schüssler-Fiorenza Rose, S. M., Contrepois, K., Moneghetti, K. J., Zhou, W., Mishra, T., Mataraso, S., Dagan-Rosenfeld, O., Ganz, A. B., Dunn, J., Hornburg, D., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0414-6 · Cited by 494
Tsai, V. W. W., Husaini, Y., Sainsbury, A., Brown, D. A., & Breit, S. N. (2018) · Cell Metabolism · doi:10.1016/j.cmet.2018.07.018 · Cited by 409
GDF15: a hormone conveying somatic distress to the brain
Lockhart, S. M., Saudek, V., & O’Rahilly, S. (2020) · Endocrine Reviews · doi:10.1210/endrev/bnaa007 · Cited by 256
Metadata
- Authors
- Wollert, K. C., Kempf, T., & Wallentin, L.
- Venue
- Clinical Chemistry
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-01-01
- Volume
- 63
- Issue
- 1
- Pages
- 140-151
- ISSN
- 0009-9147 (ISSN-L)
- 1530-8561
- OpenAlex
- W2545266774
- Cited by
- 559
- References
- 78
- FWCI
- 35.21
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Growth Differentiation Factor 15 as a Biomarker in Cardiovascular Disease. https://app.cochat.ai/papers/10.1373/clinchem.2016.255174
- The paper
- Wollert, K. C., Kempf, T., & Wallentin, L. (2017). Growth Differentiation Factor 15 as a Biomarker in Cardiovascular Disease. Clinical Chemistry. https://doi.org/10.1373/clinchem.2016.255174
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
