Metabolic Profiling Identifies 1-MetHis and 3-IPA as Potential Diagnostic Biomarkers for Patients With Acute and Chronic Heart Failure With Reduced Ejection Fraction

Author:

Kretzschmar Tom1ORCID,Westphal Julian1ORCID,Neugebauer Sophie2,Wu Jasmine M.F.1,Zeller Max1,Bogoviku Jurgen1,Bekhite Mohamed M.1ORCID,Bekfani Tarek3ORCID,Schlattmann Peter4ORCID,Kiehntopf Michael2,Franz Marcus1,Schulze P. Christian1ORCID

Affiliation:

1. Department of Internal Medicine I, Division of Cardiology (T.K., J.W., J.M.F.W., M.Z., J.B., M.M.B., M.F., P.C.S.), University Hospital Jena, Germany.

2. Institute of Clinical Chemistry and Laboratory Diagnostics (S.N., M.K.), University Hospital Jena, Germany.

3. Department of Internal Medicine I, Division of Cardiology, Angiology and Intensive Medical Care, University Hospital Magdeburg, Germany (T.B.).

4. Department of Medical Statistics, Computer Sciences and Data Science, Centre for Sepsis Control and Care, Jena University Hospital, Germany (P.S.).

Abstract

BACKGROUND: Metabolomics has become a valuable tool for identifying potential new biomarkers and metabolic profiles. It has the potential to improve the diagnosis and prognosis of different phenotypes of heart failure. To generate a distinctive metabolic profile, we assessed and compared the metabolic phenotypes of patients with acute decompensated heart failure (ADHF), patients with chronic heart failure (CHF), and healthy controls. METHODS: Plasma metabolites were analyzed by liquid-chromatography mass spectrometry/mass spectrometry and the MxP Quant 500 kit in 15 patients with ADHF, 50 patients with CHF (25 with dilated cardiomyopathy, 25 with ischemic cardiomyopathy), and 13 controls. RESULTS: Of all metabolites identified to be significantly altered, 3-indolepropionic acid and 1-methyl histidine showed the highest concentration differences in ADHF and CHF compared with control. Area under the curve–receiver operating characteristic analysis showed an area under the curve ≥0.8 for 3-indolepropionic acid and 1-methyl histidine, displaying good discrimination capabilities between control and patient cohorts. Additionally, symmetrical dimethylarginine (mean, 1.97±0.61 [SD]; P =0.01) was identified as a suitable biomarker candidate for ADHF and kynurenine (mean, 1.69±0.39 [SD]; P =0.009) for CHF when compared with control, both demonstrating an area under the curve ≥0.85. CONCLUSIONS: Our study provides novel insights into the metabolic differences between ADHF and CHF and healthy controls. We here identify new metabolites for potential diagnostic and prognostic purposes.

Publisher

Ovid Technologies (Wolters Kluwer Health)

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