TY - JOUR
T1 - Urinary peptides in heart failure
T2 - a link to molecular pathophysiology
AU - He, Tianlin
AU - Mischak, Michaela
AU - Clark, Andrew L
AU - Campbell, Ross T
AU - Delles, Christian
AU - Díez, Javier
AU - Filippatos, Gerasimos
AU - Mebazaa, Alexandre
AU - McMurray, John J V
AU - González, Arantxa
AU - Raad, Julia
AU - Stroggilos, Rafael
AU - Bosselmann, Helle S
AU - Campbell, Archie
AU - Kerr, Shona M
AU - Jackson, Colette E
AU - Cannon, Jane A
AU - Schou, Morten
AU - Girerd, Nicolas
AU - Rossignol, Patrick
AU - McConnachie, Alex
AU - Rossing, Kasper
AU - Schanstra, Joost P
AU - Zannad, Faiez
AU - Vlahou, Antonia
AU - Mullen, William
AU - Jankowski, Vera
AU - Mischak, Harald
AU - Zhang, Zhenyu
AU - Staessen, Jan A
AU - Latosinska, Agnieszka
N1 - © 2021 The Authors. European Journal of Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.
PY - 2021/4/21
Y1 - 2021/4/21
N2 - AIMS: Heart failure (HF) is a major public health concern worldwide. The diversity of HF makes it challenging to decipher the underlying complex pathological processes using single biomarkers. We examined the association between urinary peptides and HF with reduced (HFrEF), mid-range (HFmrEF) and preserved (HFpEF) ejection fraction, defined based on the European Society of Cardiology guidelines, and the links between these peptide biomarkers and molecular pathophysiology.METHODS AND RESULTS: Analysable data from 5608 participants were available in the Human Urinary Proteome database. The urinary peptide profiles from participants diagnosed with HFrEF, HFmrEF, HFpEF and controls matched for sex, age, estimated glomerular filtration rate, systolic and diastolic blood pressure, diabetes and hypertension were compared applying the Mann-Whitney test, followed by correction for multiple testing. Unsupervised learning algorithms were applied to investigate groups of similar urinary profiles. A total of 577 urinary peptides significantly associated with HF were sequenced, 447 of which (77%) were collagen fragments. In silico analysis suggested that urinary biomarker abnormalities in HF principally reflect changes in collagen turnover and immune response, both associated with fibrosis. Unsupervised clustering separated study participants into two clusters, with 83% of non-HF controls allocated to cluster 1, while 65% of patients with HF were allocated to cluster 2 (P < 0.0001). No separation based on HF subtype was detectable.CONCLUSIONS: Heart failure, irrespective of ejection fraction subtype, was associated with differences in abundance of urinary peptides reflecting collagen turnover and inflammation. These peptides should be studied as tools in early detection, prognostication, and prediction of therapeutic response.
AB - AIMS: Heart failure (HF) is a major public health concern worldwide. The diversity of HF makes it challenging to decipher the underlying complex pathological processes using single biomarkers. We examined the association between urinary peptides and HF with reduced (HFrEF), mid-range (HFmrEF) and preserved (HFpEF) ejection fraction, defined based on the European Society of Cardiology guidelines, and the links between these peptide biomarkers and molecular pathophysiology.METHODS AND RESULTS: Analysable data from 5608 participants were available in the Human Urinary Proteome database. The urinary peptide profiles from participants diagnosed with HFrEF, HFmrEF, HFpEF and controls matched for sex, age, estimated glomerular filtration rate, systolic and diastolic blood pressure, diabetes and hypertension were compared applying the Mann-Whitney test, followed by correction for multiple testing. Unsupervised learning algorithms were applied to investigate groups of similar urinary profiles. A total of 577 urinary peptides significantly associated with HF were sequenced, 447 of which (77%) were collagen fragments. In silico analysis suggested that urinary biomarker abnormalities in HF principally reflect changes in collagen turnover and immune response, both associated with fibrosis. Unsupervised clustering separated study participants into two clusters, with 83% of non-HF controls allocated to cluster 1, while 65% of patients with HF were allocated to cluster 2 (P < 0.0001). No separation based on HF subtype was detectable.CONCLUSIONS: Heart failure, irrespective of ejection fraction subtype, was associated with differences in abundance of urinary peptides reflecting collagen turnover and inflammation. These peptides should be studied as tools in early detection, prognostication, and prediction of therapeutic response.
KW - Heart Failure
KW - Humans
KW - Peptides
KW - Prognosis
KW - Stroke Volume/physiology
KW - Ventricular Function, Left/physiology
U2 - 10.1002/ejhf.2195
DO - 10.1002/ejhf.2195
M3 - Article
C2 - 33881206
VL - 23
SP - 1875
EP - 1887
JO - European Journal of Heart Failure
JF - European Journal of Heart Failure
SN - 1388-9842
IS - 11
ER -