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

BACKGROUND: Progressive myocardial remodeling (MR) in chronic heart failure (CHF) leads to aggravation of systolic dysfunction (SD) and clinical manifestations. Identification of metabolomic markers of these processes may help in the search for new therapeutic approaches aimed at achieving reversibility of MR and improving prognosis in patients with CHF. METHODS: To determine the relationship between plasma acylcarnitine (ACs) levels, MR parameters and clinical characteristics, in patients with CHF of ischemic etiology (n = 79) and patients with coronary heart disease CHD (n = 19) targeted analysis of 30 ACs was performed by flow injection analysis mass spectrometry. RESULTS: Significant differences between cohorts were found for the levels of 11 ACs. Significant positive correlations (r > 0.3) between the medium- and long-chain ACs (MCACs and LCACs) and symptoms (CHF NYHA functional class (FC); r = 0.31-0.39; p < 0.05); negative correlation (r = -0.31-0.34; p < 0.05) between C5-OH and FC was revealed. Positive correlations of MCACs and LCACs (r = 0.31-0.48; p < 0.05) with the left atrium size and volume, the right atrium volume, right ventricle, and the inferior vena cava sizes, as well as the pulmonary artery systolic pressure level were shown. A negative correlation between C18:1 and left ventricular ejection fraction (r = -0.31; p < 0.05) was found. However, a decrease in levels compared to referent values of ACs with medium and long chain lengths was 50% of the CHF-CHD cohort. Carnitine deficiency was found in 6% and acylcarnitine deficiency in 3% of all patients with chronic heart disease. CONCLUSIONS: ACs may be used in assessing the severity of the clinical manifestations and MR. ACs are an important locus to study in terms of altered metabolic pathways in patients with CHF of ischemic etiology and SD. Further larger prospective trials are warranted and needed to determine the potential benefits to treat patients with CV diseases with aberrate AC levels.

Target Metabolome Profiling-Based Machine Learning as a Diagnostic Approach for Cardiovascular Diseases in Adults.

Metabolomics is a promising technology for the application of translational medicine to cardiovascular risk. Here, we applied a liquid chromatography/tandem mass spectrometry approach to explore the associations between plasma concentrations of amino acids, methylarginines, acylcarnitines, and tryptophan catabolism metabolites and cardiometabolic risk factors in patients diagnosed with arterial hypertension (HTA) (n = 61), coronary artery disease (CAD) (n = 48), and non-cardiovascular disease (CVD) individuals (n = 27). In total, almost all significantly different acylcarnitines, amino acids, methylarginines, and intermediates of the kynurenic and indolic tryptophan conversion pathways presented increased (p < 0.05) in concentration levels during the progression of CVD, indicating an association of inflammation, mitochondrial imbalance, and oxidative stress with early stages of CVD. Additionally, the random forest algorithm was found to have the highest prediction power in multiclass and binary classification patients with CAD, HTA, and non-CVD individuals and globally between CVD and non-CVD individuals (accuracy equal to 0.80 and 0.91, respectively). Thus, the present study provided a complex approach for the risk stratification of patients with CAD, patients with HTA, and non-CVD individuals using targeted metabolomics profiling.

Relationship between the plasma acylcarnitine profile and cardiometabolic risk factors in adults diagnosed with cardiovascular diseases.

The development of cardiovascular diseases (CVDs) is often asymptomatic. Identification of initial indicators of cardiometabolic disruption may assist in its early detection. The objective was to determine the relationships between plasma acylcarnitines (ACs) and cardiometabolic risk factors in adults with and without CVDs. The AC profile in human plasma of healthy controls [non-CVD group, n = 13)] and individuals diagnosed with CVDs (CVD group, n = 34) were compared. A targeted analysis of 29 ACs was performed using flow injection analysis-tandem mass spectrometry. There were significant direct correlations (p < 0.05) between ACs and cardiometabolic risk factors. Comparing the groups after adjustment for covariates, showed that the ACs that were best differentiated (p < 0.05) between the two groups and that presented "good" diagnostic accuracy were carnitine [30.7 (25.5-37.7) vs. 37.7 (32.3-45.0) µM], the short-chain ACs: acetylcarnitine [8.9 (7.4-10.2) vs. 11.9 (9.2-14.4) µM] and isovalerylcarnitine [0.10 (0.06-0.13) vs. 0.13 (0.10-0.16) µM], and the medium-chain ACs: hexanoylcarnitine [0.04 (0.03-0.05) vs. 0.06 (0.05-0.07) µM] and decenoylcarnitine [0.18 (0.12-0.22) vs. 0.22 (0.17-0.32) µM]. This assessment contributes to the identification of the unique metabolic features exhibited in association with cardiometabolic risk in adults diagnosed with CVD. The altered metabolites have the potential to be used as biomarkers for early detection of CVD.

Pathogenesis of sudden unexpected death in a clinical trial of patients with myocardial infarction and left ventricular dysfunction, heart failure, or both.

BACKGROUND: The frequency of sudden unexpected death is highest in the early post-myocardial infarction (MI) period; nevertheless, 2 recent trials showed no improvement in mortality with early placement of an implantable cardioverter-defibrillator after MI. METHODS AND RESULTS: To better understand the pathophysiological events that lead to sudden death after MI, we assessed autopsy records in a series of cases classified as sudden death events in patients from the VALsartan In Acute myocardial infarctioN Trial (VALIANT). Autopsy records were available in 398 cases (14% of deaths). We determined that 105 patients had clinical circumstances consistent with sudden death. On the basis of the autopsy findings, we assessed the probable cause of sudden death and evaluated how these causes varied with time after MI. Of 105 deaths considered sudden on clinical grounds, autopsy suggested the following causes: 3 index MIs in the first 7 days (2.9%); 28 recurrent MIs (26.6%); 13 cardiac ruptures (12.4%); 4 pump failures (3.8%); 2 other cardiovascular causes (stroke or pulmonary embolism; 1.9%); and 1 noncardiovascular cause (1%). Fifty-four cases (51.4%) had no acute specific autopsy evidence other than the index MI and were thus presumed arrhythmic. The percentage of sudden death due to recurrent MI or rupture was highest in the first month after the index MI. By contrast, after 3 months, the percentage of presumed arrhythmic death was higher than recurrent MI or rupture (chi(2)=23.3, P<0.0001). CONCLUSIONS: Recurrent MI or cardiac rupture accounts for a high proportion of sudden death in the early period after acute MI, whereas arrhythmic death may be more likely subsequently. These findings may help explain the lack of benefit of early implantable cardioverter-defibrillator therapy.

Newly diagnosed and previously known diabetes mellitus and 1-year outcomes of acute myocardial infarction: the VALsartan In Acute myocardial iNfarcTion (VALIANT) trial.

BACKGROUND: A prior diagnosis of diabetes mellitus is associated with adverse outcomes after acute myocardial infarction (MI), but the risk associated with a new diagnosis of diabetes in this setting has not been well defined. METHODS AND RESULTS: We assessed the risk of death and major cardiovascular events associated with previously known and newly diagnosed diabetes by studying 14,703 patients with acute MI enrolled in the VALsartan In Acute myocardial iNfarcTion (VALIANT) trial. Patients were grouped by diabetic status: previously known diabetes (insulin use or diagnosis of diabetes before MI, n=3400, 23%); newly diagnosed diabetes (use of diabetic therapy or diabetes diagnosed at randomization [median 4.9 d after infarction], but no known diabetes at presentation, n=580, 4%); or no diabetes (n=10,719). Patients with newly diagnosed diabetes were younger and had fewer comorbid conditions than did patients with previously known diabetes. At 1 year after enrollment, patients with previously known and newly diagnosed diabetes had similarly increased adjusted risks of mortality (hazard ratio [HR] 1.43; 95% confidence interval [CI], 1.29 to 1.59 and HR, 1.50; 95% CI, 1.21 to 1.85, respectively) and cardiovascular events (HR, 1.37; 95% CI, 1.27 to 1.48 and HR, 1.34; 95% CI, 1.14 to 1.56). CONCLUSIONS: Diabetes mellitus, whether newly diagnosed or previously known, is associated with poorer long-term outcomes after MI in high-risk patients. The poor prognosis of patients with newly diagnosed diabetes, despite having baseline characteristics similar to those of patients without diabetes, supports the idea that metabolic abnormalities contribute to their adverse outcomes.