Beuchel, Carl
[Author];
Dittrich, Julia
[Author];
Pott, Janne
[Author];
Henger, Sylvia
[Author];
Beutner, Frank
[Author];
Isermann, Berend
[Author];
Loeffler, Markus
[Author];
Thiery, Joachim
[Author];
Ceglarek, Uta
[Author];
Scholz, Markus
[Author]
Whole Blood Metabolite Profiles Reflect Changes in Energy Metabolism in Heart Failure
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Media type:
E-Article
Title:
Whole Blood Metabolite Profiles Reflect Changes in Energy Metabolism in Heart Failure
Contributor:
Beuchel, Carl
[Author];
Dittrich, Julia
[Author];
Pott, Janne
[Author];
Henger, Sylvia
[Author];
Beutner, Frank
[Author];
Isermann, Berend
[Author];
Loeffler, Markus
[Author];
Thiery, Joachim
[Author];
Ceglarek, Uta
[Author];
Scholz, Markus
[Author]
Description:
A variety of atherosclerosis and cardiovascular disease (ASCVD) phenotypes are tightly linked to changes in the cardiac energy metabolism that can lead to a loss of metabolic flexibility and to unfavorable clinical outcomes. We conducted an association analysis of 31 ASCVD phenotypes and 97 whole blood amino acids, acylcarnitines and derived ratios in the LIFE-Adult (n = 9646) and LIFE-Heart (n = 5860) studies, respectively. In addition to hundreds of significant associations, a total of 62 associations of six phenotypes were found in both studies. Positive associations of various amino acids and a range of acylcarnitines with decreasing cardiovascular health indicate disruptions in mitochondrial, as well as peroxisomal fatty acid oxidation. We complemented our metabolite association analyses with whole blood and peripheral blood mononuclear cell (PBMC) gene-expression analyses of fatty acid oxidation and ketone-body metabolism related genes. This revealed several differential expressions for the heart failure biomarker N-terminal prohormone of brain natriuretic peptide (NT-proBNP) in peripheral blood mononuclear cell (PBMC) gene expression. Finally, we constructed and compared three prediction models of significant stenosis in the LIFE-Heart study using (1) traditional risk factors only, (2) the metabolite panel only and (3) a combined model. Area under the receiver operating characteristic curve (AUC) comparison of these three models shows an improved prediction accuracy for the combined metabolite and classical risk factor model (AUC = 0.78, 95%-CI: 0.76–0.80). In conclusion, we improved our understanding of metabolic implications of ASCVD phenotypes by observing associations with metabolite concentrations and gene expression of the mitochondrial and peroxisomal fatty acid oxidation. Additionally, we demonstrated the predictive potential of the metabolite profile to improve classification of patients with significant stenosis.