A network analysis to compare biomarker profiles in patients with and without diabetes mellitus in acute heart failure. (21st June 2017)

• Record Type: Journal Article
• Authors: 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.
• Is Part Of: European journal of heart failure. Volume 19:Number 10(2017)
• Page Start: 1310
• Page End: 1320
• Publication Date: 2017-06-21
• Subjects: Acute heart failure -- Diabetes -- Network analysis -- Biomarkers -- Periostin -- Inflammation; Heart failure -- Periodicals; Heart Failure -- Periodicals; Insuffisance cardiaque -- Périodiques; Heart failure; Periodicals; 616.129005
• DOI: 10.1002/ejhf.912
• Languages: English
• ISSNs: 1388-9842

Abstract

Abstract: 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.