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A network analysis to compare biomarker profiles in patients with and without diabetes mellitus in acute heart failure
Author(s) -
Sharma Abhinav,
Demissei Biniyam G.,
Tromp Jasper,
Hillege Hans L.,
Cleland John G.,
O'Connor Christopher M.,
Metra Marco,
Ponikowski Piotr,
Teerlink John R.,
Davison Beth A.,
Givertz Michael M.,
Bloomfield Daniel M.,
Dittrich Howard,
van Veldhuisen Dirk J.,
Cotter Gad,
Ezekowitz Justin A.,
Khan Mohsin A.F.,
Voors Adriaan A.
Publication year - 2017
Publication title -
european journal of heart failure
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.149
H-Index - 133
eISSN - 1879-0844
pISSN - 1388-9842
DOI - 10.1002/ejhf.912
Subject(s) - medicine , diabetes mellitus , periostin , heart failure , biomarker , pathophysiology , creatinine , cardiology , renal function , type 2 diabetes , endocrinology , biochemistry , chemistry , extracellular matrix , biology , microbiology and biotechnology
Aims It is unclear whether distinct pathophysiological processes are present among patients with acute heart failure ( AHF ), with and without diabetes. Network analysis of biomarkers may identify correlative associations that reflect different pathophysiological pathways. Methods and results We analysed a panel of 48 circulating biomarkers measured within 24 h of admission for AHF in a subset of patients enrolled in the PROTECT trial. In patients with and without diabetes, we performed a network analysis to identify correlations between measured biomarkers. Compared with patients without diabetes ( n = 1111), those with diabetes ( n = 922) had a higher prevalence of ischaemic heart disease and traditional coronary risk factors. After multivariable adjustment, patients with and without diabetes had significantly different levels of biomarkers across a spectrum of pathophysiological domains, including inflammation ( TNFR ‐1a, periostin), cardiomyocyte stretch ( BNP ), angiogenesis ( VEGFR , angiogenin), and renal function ( NGAL , KIM-1 ) (adjusted P ‐value <0.05). Among patients with diabetes, network analysis revealed that periostin strongly clustered with C‐reactive protein and interleukin‐6. Furthermore, renal markers (creatinine and NGAL ) closely associated with potassium and glucose. These findings were not seen among patients without diabetes. Conclusion Patients with AHF and diabetes, compared with those without diabetes, have distinct biomarker profiles. Network analysis suggests that cardiac remodelling, inflammation, and fibrosis are closely associated with each other in patients with diabetes. Furthermore, potassium levels may be sensitive to changes in renal function as reflected by the strong renal–potassium–glucose correlation. These findings were not seen among patients without diabetes and may suggest distinct pathophysiological processes among AHF patients with diabetes.