Abhinav Sharma1,2, Biniyam G Demissei1,3, Jasper Tromp1, Hans L Hillege1,3, John G Cleland4, Christopher M O'Connor5, Marco Metra6, Piotr Ponikowski7, John R Teerlink8, Beth A Davison9, Michael M Givertz10, Daniel M Bloomfield11, Howard Dittrich12, Dirk J van Veldhuisen1, Gad Cotter9, Justin A Ezekowitz2,13, Mohsin A F Khan1,14,15, Adriaan A Voors1. 1. Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. 2. Department of Cardiology, Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, Alberta, Canada. 3. Department of Epidemiology, University Medical Center Groningen, Groningen, The Netherlands. 4. Imperial College, London, UK. 5. Inova Heart and Vascular Institute, Fairfax, VA, USA. 6. University of Brescia, Brescia, Italy. 7. Medical University, Clinical Military Hospital, Wroclaw, Poland. 8. University of California at San Francisco and San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA. 9. Momentum Research, Durham, NC, USA. 10. Brigham and Women's Hospital, Boston, MA, USA. 11. Merck & Co., Inc., Kenilworth, NJ, USA. 12. Abboud Cardiovascular Research Center, University of Iowa Carver College of Medicine, Iowa City, IA, USA. 13. Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada. 14. Department of Experimental Cardiology, Academic Medical Center, Amsterdam, The Netherlands. 15. Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, Amsterdam, The Netherlands.
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.
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.
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