Association between growth differentiation factor-15 and adverse outcomes among patients with heart failure: A systematic literature review

生长分化因子-15与心力衰竭患者不良预后之间的关联:一项系统性文献综述

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Abstract

Growth differentiation factor-15 (GDF-15) is an emerging biomarker in several conditions. This SLR, conducted following PRISMA guidelines, examined the association between GDF-15 concentration and range of adverse outcomes in patients with heart failure (HF). Publications were identified from Embase® and Medline® bibliographic databases between January 1, 2014, and August 23, 2022 (congress abstracts: January 1, 2020, to August 23, 2022). Sixty-three publications met the eligibility criteria (55 manuscripts and 8 abstracts; 45 observational studies and 18 post hoc analyses of randomized controlled trials [RCTs]). Of the 19 outcomes identified, the most frequently reported longitudinal outcomes were mortality (n = 32 studies; all-cause [n = 27] or cardiovascular-related [n = 6]), composite outcomes (n = 28; most commonly mortality ± hospitalization/rehospitalization [n = 19]), and hospitalization/re-hospitalization (n = 11). The most common cross-sectional outcome was renal function (n = 22). Among longitudinal studies assessing independent relationships with outcomes using multivariate analyses (MVA), a significant increase in risk associated with higher baseline GDF-15 concentration was found in 22/24 (92 %) studies assessing all-cause mortality, 4/5 (80 %) assessing cardiovascular-related mortality, 13/19 (68 %) assessing composite outcomes, and 4/8 (50 %) assessing hospitalization/rehospitalization. All (7/7; 100 %) of the cross-sectional studies assessing the relationship with renal function by MVA, and 3/4 (75 %) assessing exercise capacity, found poorer outcomes associated with higher baseline GDF-15 concentrations. This SLR suggests GDF-15 is an independent predictor of mortality and other adverse but nonfatal outcomes in patients with HF. A better understanding of the prognostic role of GDF-15 in HF could improve clinical risk prediction models and potentially help optimize treatment regimens.

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