Whole Blood Metabolite Profiles Reflect Changes in Energy Metabolism in Heart Failure

Abstract

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.

Relationship of Acylcarnitines to Myocardial Ischemic Remodeling and Clinical Manifestations in Chronic Heart Failure

“…AСs are of interest because in the field of metabolomic profiling, there is increasing evidence for the use of these metabolites as markers of CVD: insulin resistance [11], arterial hypertension [12], atrial fibrillation [13,14], CHD [15] and CHF [16][17][18]. In previous studies presented in scientific databases, we did not find a direct comparison of AC levels in patients with CHD and CHF of ischemic etiology (Table 5).…”

A personalized probabilistic approach to ovarian cancer diagnostics

“…Metabolic profiles are widely viewed as a molecular phenotype reflective of underlying collective information encoded at the genome level and realized at the transcriptome and proteome levels. As such, metabolic profiles have long been considered promising indicators of cancer and other complex diseases [ 8 , 9 ].…”

“…In recent years, various computational methods, including machine learning (ML), have been applied in efforts to identify patterns embedded within large omics datasets (e.g., genomic/proteomic/metabolomic) that may constitute an accurate diagnostic of cancer [ 5 , 6 ] and other diseases [ 7 ]. For example, perturbations of metabolic levels in the blood and/or other body fluids have long been considered promising indicators of cancer and other diseases [ 8 , 9 ] because metabolites constitute end points of many, if not most, of the molecular processes underlying biological functions. As such, metabolic profiles have been proposed as a molecular phenotype of biological systems, reflective of collective information encoded at the genome level and realized at the transcriptome and proteome levels [ 10 ].…”

The Association of Metabolomic Profiles of a Healthy Lifestyle with Heart Failure Risk in a Prospective Study

“…Existing studies have found that metabolites related to coffee intake are associated with the risk of diabetes through serum metabolomics, and these metabolites can add the predictivity of the risk of diabetes [16]. The blood metabolomics of HF patients are different from those of healthy individuals [17]. Moreover, smoking, as an important risk factor for heart failure, can cause changes in a variety of metabolites in NMR metabolomics profiling [14].…”