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

Dimasi C.G., Darby J.R., Morrison J.L.

Alhasaniah A.H.

Carnitine is a medically needful nutrient that contributes in the production of energy and the metabolism of fatty acids. Bioavailability is higher in vegetarians than in people who eat meat. Deficits in carnitine transporters occur as a result of genetic mutations or in combination with other illnesses such like hepatic or renal disease. Carnitine deficit can arise in diseases such endocrine maladies, cardiomyopathy, diabetes, malnutrition, aging, sepsis, and cirrhosis due to abnormalities in carnitine regulation. The exogenously provided molecule is obviously useful in people with primary carnitine deficits, which can be life-threatening, and also some secondary deficiencies, including such organic acidurias: by eradicating hypotonia, muscle weakness, motor skills, and wasting are all improved l-carnitine (LC) have reported to improve myocardial functionality and metabolism in ischemic heart disease patients, as well as athletic performance in individuals with angina pectoris. Furthermore, although some intriguing data indicates that LC could be useful in a variety of conditions, including carnitine deficiency caused by long-term total parenteral supplementation or chronic hemodialysis, hyperlipidemias, and the prevention of anthracyclines and valproate-induced toxicity, such findings must be viewed with caution.

Moskaleva N.E., Shestakova K.M., Kukharenko A.V., Markin P.A., Kozhevnikova M.V., Korobkova E.O., Brito A., Baskhanova S.N., Mesonzhnik N.V., Belenkov Y.N., Pyatigorskaya N.V., Tobolkina E., Rudaz S., Appolonova S.A.

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.

Zhong J., Wu D., Zeng Y., Wu G., Zheng N., Huang W., Li Y., Tao X., Zhu W., Sheng L., Shen X., Zhang W., Zhu R., Li H.

Stable coronary artery disease (SCAD) is an early stage of CAD development. It is important to understand the pathogenesis of SCAD and find out the possible prevention and control targets for delaying the progression of CAD.

Li M., Ning Y., Tse G., Saguner A.M., Wei M., Day J.D., Luo G., Li G.

Atrial cardiomyopathy refers to structural and electrical remodelling of the atria, which can lead to impaired mechanical function. While historical studies have implicated atrial fibrillation as the leading cause of cardioembolic stroke, atrial cardiomyopathy may be an important, underestimated contributor. To date, the relationship between atrial cardiomyopathy, atrial fibrillation, and cardioembolic stroke remains obscure. This review summarizes the pathogenesis of atrial cardiomyopathy, with a special focus on neurohormonal and inflammatory mechanisms, as well as the role of adipose tissue, especially epicardial fat in atrial remodelling. It reviews the current evidence implicating atrial cardiomyopathy as a cause of embolic stroke, with atrial fibrillation as a lagging marker of an increased thrombogenic atrial substrate. Finally, it discusses the potential of antithrombotic therapy in embolic stroke with undetermined source and appraises the available diagnostic techniques for atrial cardiomyopathy, including imaging techniques such as echocardiography, computed tomography, and magnetic resonance imaging as well as electroanatomic mapping, electrocardiogram, biomarkers, and genetic testing. More prospective studies are needed to define the relationship between atrial cardiomyopathy, atrial fibrillation, and embolic stroke and to establish a prompt diagnosis and specific treatment strategies in these patients with atrial cardiomyopathy for the secondary and even primary prevention of embolic stroke.

Dambrova M., Makrecka-Kuka M., Kuka J., Vilskersts R., Nordberg D., Attwood M.M., Smesny S., Sen Z.D., Guo A.C., Oler E., Tian S., Zheng J., Wishart D.S., Liepinsh E., Schiöth H.B.

Acylcarnitines are fatty acid metabolites that play important roles in many cellular energy metabolism pathways. They have historically been used as important diagnostic markers for inborn errors of fatty acid oxidation and are being intensively studied as markers of energy metabolism, deficits in mitochondrial and peroxisomal β-oxidation activity, insulin resistance, and physical activity. Acylcarnitines are increasingly being identified as important indicators in metabolic studies of many diseases, including metabolic disorders, cardiovascular diseases, diabetes, depression, neurologic disorders, and certain cancers. The US Food and Drug Administration-approved drug L-carnitine, along with short-chain acylcarnitines (acetylcarnitine and propionylcarnitine), is now widely used as a dietary supplement. In light of their growing importance, we have undertaken an extensive review of acylcarnitines and provided a detailed description of their identity, nomenclature, classification, biochemistry, pathophysiology, supplementary use, potential drug targets, and clinical trials. We also summarize these updates in the Human Metabolome Database, which now includes information on the structures, chemical formulae, chemical/spectral properties, descriptions, and pathways for 1240 acylcarnitines. This work lays a solid foundation for identifying, characterizing, and understanding acylcarnitines in human biosamples. We also discuss the emerging opportunities for using acylcarnitines as biomarkers and as dietary interventions or supplements for many wide-ranging indications. The opportunity to identify new drug targets involved in controlling acylcarnitine levels is also discussed. Significance Statement This review provides a comprehensive overview of acylcarnitines, including their nomenclature, structure and biochemistry, and use as disease biomarkers and pharmaceutical agents. We present updated information contained in the Human Metabolome Database website as well as substantial mapping of the known biochemical pathways associated with acylcarnitines, thereby providing a strong foundation for further clarification of their physiological roles.

Selvaraj S., Fu Z., Jones P., Kwee L.C., Windsor S.L., Ilkayeva O., Newgard C.B., Margulies K.B., Husain M., Inzucchi S.E., McGuire D.K., Pitt B., Scirica B.M., Lanfear D.E., Nassif M.E., et. al.

Background: Sodium-glucose cotransporter-2 inhibitors are foundational therapy in patients with heart failure with reduced ejection fraction (HFrEF), but underlying mechanisms of benefit are not well defined. We sought to investigate the relationships between sodium-glucose cotransporter-2 inhibitor treatment, changes in metabolic pathways, and outcomes using targeted metabolomics. Methods: DEFINE-HF (Dapagliflozin Effects on Biomarkers, Symptoms and Functional Status in Patients With HF With Reduced Ejection Fraction) was a placebo-controlled trial of dapagliflozin in HFrEF. We performed targeted mass spectrometry profiling of 63 metabolites (45 acylcarnitines [markers of fatty acid oxidation], 15 amino acids, and 3 conventional metabolites) in plasma samples at randomization and 12 weeks. Using mixed models, we identified principal components analysis–defined metabolite clusters that changed differentially with treatment and examined the relationship between change in metabolite clusters and change in Kansas City Cardiomyopathy Questionnaire scores and NT-proBNP (N-terminal probrain natriuretic peptide). Models were adjusted for relevant clinical covariates and nominal P <0.05 with false discovery rate–adjusted P <0.10 was used to determine statistical significance. Results: Among the 234 DEFINE-HF participants with targeted metabolomic data, the mean age was 62.0±11.1 years, 25% were women, 38% were Black, and mean ejection fraction was 27±8%. Dapagliflozin increased ketone-related and short-chain acylcarnitine as well as medium-chain acylcarnitine principal components analysis–defined metabolite clusters compared with placebo (nominal P =0.01, false discovery rate–adjusted P =0.08 for both clusters). However, ketosis (β-hydroxybutyrate levels >500 μmol/L) was achieved infrequently (3 [2.5%] in dapagliflozin arm versus 1 [0.9%] in placebo arm) and supraphysiologic levels were not observed. Increases in long-chain acylcarnitine, long-chain dicarboxylacylcarnitine, and aromatic amino acid metabolite clusters were associated with decreases in Kansas City Cardiomyopathy Questionnaire scores (ie, worse quality of life) and increases in NT-proBNP levels, without interaction by treatment group. Conclusions: In this study of targeted metabolomics in a placebo-controlled trial of sodium-glucose cotransporter-2 inhibitors in HFrEF, we observed effects of dapagliflozin on key metabolic pathways, supporting a role for altered ketone and fatty acid biology with sodium-glucose cotransporter-2 inhibitors in patients with HFrEF. Only physiologic levels of ketosis were observed. In addition, we identified several metabolic biomarkers associated with adverse HFrEF outcomes. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02653482.

Girerd N., Cleland J., Anker S.D., Byra W., Lam C.S., Lapolice D., Mehra M.R., van Veldhuisen D.J., Bresso E., Lamiral Z., Greenberg B., Zannad F.

Patients with heart failure (HF) and coronary artery disease (CAD) have a high risk for cardiovascular (CV) events including HF hospitalization, stroke, myocardial infarction (MI) and sudden cardiac death (SCD). The present study evaluated associations of proteomic biomarkers with CV outcome in patients with CAD and HF with reduced ejection fraction (HFrEF), shortly after a worsening HF episode. We performed a case–control study within the COMMANDER HF international, double-blind, randomized placebo-controlled trial investigating the effects of the factor-Xa inhibitor rivaroxaban. Patients with the following first clinical events: HF hospitalization, SCD and the composite of MI or stroke were matched with corresponding controls for age, sex and study drug. Plasma concentrations of 276 proteins with known associations with CV and cardiometabolic mechanisms were analyzed. Results were corrected for multiple testing using false discovery rate (FDR). In 485 cases and 455 controls, 49 proteins were significantly associated with clinical events of which seven had an adjusted FDR < 0.001 (NT-proBNP, BNP, T-cell immunoglobulin and mucin domain containing 4 (TIMD4), fibroblast growth factor 23 (FGF-23), growth differentiation factor-15 (GDF-15), pulmonary surfactant-associated protein D (PSP-D) and Spondin-1 (SPON1)). No significant interactions were identified between the type of clinical event (MI/stroke, SCD or HFH) and specific biomarkers (all interaction FDR > 0.20). When adding the biomarkers significantly associated with the above outcome to a clinical model (including NT-proBNP), the C-index increase was 0.057 (0.033–0.082), p < 0.0001 and the net reclassification index was 54.9 (42.5 to 67.3), p < 0.0001. In patients with HFrEF and CAD following HF hospitalization, we found that NT-proBNP, BNP, TIMD4, FGF-23, GDF-15, PSP-D and SPON1, biomarkers broadly associated with inflammation and remodeling mechanistic pathways, were strong but indiscriminate predictors of a variety of individual CV events.

Karagiannidis E., Moysidis D.V., Papazoglou A.S., Panteris E., Deda O., Stalikas N., Sofidis G., Kartas A., Bekiaridou A., Giannakoulas G., Gika H., Theodoridis G., Sianos G.

Diabetes mellitus (DM) and coronary artery disease (CAD) constitute inter-related clinical entities. Biomarker profiling emerges as a promising tool for the early diagnosis and risk stratification of either DM or CAD. However, studies assessing the predictive capacity of novel metabolomics biomarkers in coexistent CAD and DM are scarce. This post-hoc analysis of the CorLipid trial (NCT04580173) included 316 patients with CAD and comorbid DM who underwent emergency or elective coronary angiography due to acute or chronic coronary syndrome. Cox regression analyses were performed to identify metabolomic predictors of the primary outcome, which was defined as the composite of major adverse cardiovascular or cerebrovascular events (MACCE: cardiovascular death, myocardial infarction, stroke, major bleeding), repeat unplanned revascularizations and cardiovascular hospitalizations. Linear regression analyses were also performed to detect significant predictors of CAD complexity, as assessed by the SYNTAX score. After a median 2-year follow up period (IQR = 0.7 years), the primary outcome occurred in 69 (21.8%) of patients. Acylcarnitine ratio C4/C18:2, apolipoprotein (apo) B, history of heart failure (HF), age > 65 years and presence of acute coronary syndrome were independent predictors of the primary outcome in diabetic patients with CAD (aHR = 1.89 [1.09, 3.29]; 1.02 [1.01, 1.04]; 1.28 [1.01, 1.41]; 1.04 [1.01, 1.05]; and 1.12 [1.05–1.21], respectively). Higher levels of ceramide ratio C24:1/C24:0, acylcarnitine ratio C4/C18:2, age > 65 and peripheral artery disease were independent predictors of higher CAD complexity (adjusted β = 7.36 [5.74, 20.47]; 3.02 [0.09 to 6.06]; 3.02 [0.09, 6.06], respectively), while higher levels of apoA1 were independent predictors of lower complexity (adjusted β= − 0.65 [− 1.31, − 0.02]). In patients with comorbid DM and CAD, novel metabolomic biomarkers and metabolomics-based prediction models could be recruited to predict clinical outcomes and assess the complexity of CAD, thereby enabling the integration of personalized medicine into routine clinical practice. These associations should be interpreted taking into account the observational nature of this study, and thus, larger trials are needed to confirm its results and validate them in different and larger diabetic populations.

Johri A.M., Hétu M., Heyland D.K., Herr J.E., Korol J., Froese S., Norman P.A., Day A.G., Matangi M.F., Michos E.D., LaHaye S.A., Saunders F.W., Spence J.D.

L-carnitine (L-C), a ubiquitous nutritional supplement, has been investigated as a potential therapy for cardiovascular disease, but its effects on human atherosclerosis are unknown. Clinical studies suggest improvement of some cardiovascular risk factors, whereas others show increased plasma levels of pro-atherogenic trimethylamine N-oxide. The primary aim was to determine whether L-C therapy led to progression or regression of carotid total plaque volume (TPV) in participants with metabolic syndrome (MetS). This was a phase 2, prospective, double blinded, randomized, placebo-controlled, two-center trial. MetS was defined as ≥ 3/5 cardiac risk factors: elevated waist circumference; elevated triglycerides; reduced HDL-cholesterol; elevated blood pressure; elevated glucose or HbA1c; or on treatment. Participants with a baseline TPV ≥ 50 mm3 were randomized to placebo or 2 g L-C daily for 6 months. The primary outcome was the percent change in TPV over 6 months. In 157 participants (L-C N = 76, placebo N = 81), no difference in TPV change between arms was found. The L-C group had a greater increase in carotid atherosclerotic stenosis of 9.3% (p = 0.02) than the placebo group. There was a greater increase in total cholesterol and LDL-C levels in the L-C arm. Though total carotid plaque volume did not change in MetS participants taking L-C over 6-months, there was a concerning progression of carotid plaque stenosis. The potential harm of L-C in MetS and its association with pro-atherogenic metabolites raises concerns for its further use as a potential therapy and its widespread availability as a nutritional supplement. Trial registration: ClinicalTrials.gov, NCT02117661, Registered April 21, 2014, https://clinicaltrials.gov/ct2/show/NCT02117661 .

Beuchel C., Dittrich J., Pott J., Henger S., Beutner F., Isermann B., Loeffler M., Thiery J., Ceglarek U., Scholz M.

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.

Deda O., Panteris E., Meikopoulos T., Begou O., Mouskeftara T., Karagiannidis E., Papazoglou A.S., Sianos G., Theodoridis G., Gika H.

Recent studies support that acylcarnitines exert a significant role in cardiovascular disease development and progression. The aim of this metabolomics-based study was to investigate the association of serum acylcarnitine levels with coronary artery disease (CAD) severity, as assessed via SYNTAX Score. Within the context of the prospective CorLipid trial (NCT04580173), the levels of 13 circulating acylcarnitines were accurately determined through a newly developed HILIC-MS/MS method in 958 patients undergoing coronary angiography in the AHEPA University Hospital of Thessaloniki, Greece. Patients presenting with acute coronary syndrome had significantly lower median acylcarnitine C8, C10, C16, C18:1 and C18:2 values, compared to patients with chronic coronary syndrome (p = 0.012, 0.007, 0.018, 0.011 and <0.001, respectively). Among CAD subgroups, median C5 levels were significantly decreased in unstable angina compared to STEMI (p = 0.026), while median C10, C16, C18:1 and C18:2 levels were higher in stable angina compared to STEMI (p = 0.019 p = 0.012, p = 0.013 and p < 0.001, respectively). Moreover, median C2, C3, C4 and C8 levels were significantly elevated in patients with diabetes mellitus (p < 0.001, <0.001, 0.029 and 0.011, respectively). Moreover, short-chain acylcarnitine C2, C4, C5 and C6 levels were elevated in patients with heavier calcification and lower left ventricular ejection fraction (LVEF) % (all p-values less than 0.05). With regard to CAD severity, median C4 and C5 levels were elevated and C16 and C18:2 levels were reduced in the high CAD complexity group with SYNTAX Score > 22 (p = 0.002, 0.024, 0.044 and 0.012, respectively), indicating a potential prognostic capability of those metabolites and of the ratio C4/C18:2 for the prediction of CAD severity. In conclusion, serum acylcarnitines could serve as clinically useful biomarkers leading to a more individualized management of patients with CAD, once further clinically oriented metabolomics-based studies provide similar evidence.

Gander J., Carrard J., Gallart-Ayala H., Borreggine R., Teav T., Infanger D., Colledge F., Streese L., Wagner J., Klenk C., Nève G., Knaier R., Hanssen H., Schmidt-Trucksäss A., Ivanisevic J.

Coronary artery disease (CAD) remains the leading cause of death worldwide. Expanding patients' metabolic phenotyping beyond clinical chemistry investigations could lead to earlier recognition of disease onset and better prevention strategies. Additionally, metabolic phenotyping, at the molecular species level, contributes to unravel the roles of metabolites in disease development. In this cross-sectional study, we investigated clinically healthy individuals (n = 116, 65% male, 70.8 ± 8.7 years) and patients with CAD (n = 54, 91% male, 67.0 ± 11.5 years) of the COmPLETE study. We applied a high-coverage quantitative liquid chromatography-mass spectrometry approach to acquire a comprehensive profile of serum acylcarnitines, free carnitine and branched-chain amino acids (BCAAs), as markers of mitochondrial health and energy homeostasis. Multivariable linear regression analyses, adjusted for confounders, were conducted to assess associations between metabolites and CAD phenotype. In total, 20 short-, medium- and long-chain acylcarnitine species, along with L-carnitine, valine and isoleucine were found to be significantly (adjusted p ≤ 0.05) and positively associated with CAD. For 17 acylcarnitine species, associations became stronger as the number of affected coronary arteries increased. This implies that circulating acylcarnitine levels reflect CAD severity and might play a role in future patients' stratification strategies. Altogether, CAD is characterized by elevated serum acylcarnitine and BCAA levels, which indicates mitochondrial imbalance between fatty acid and glucose oxidation.

Jiang M., Xie X., Cao F., Wang Y.

Ischemic heart disease refers to myocardial degeneration, necrosis, and fibrosis caused by coronary artery disease. It can lead to severe left ventricular dysfunction (LVEF ≤ 35–40%) and is a major cause of heart failure (HF). In each contraction, myocardium is subjected to a variety of mechanical forces, such as stretch, afterload, and shear stress, and these mechanical stresses are clinically associated with myocardial remodeling and, eventually, cardiac outcomes. Mitochondria produce 90% of ATP in the heart and participate in metabolic pathways that regulate the balance of glucose and fatty acid oxidative phosphorylation. However, altered energetics and metabolic reprogramming are proved to aggravate HF development and progression by disturbing substrate utilization. This review briefly summarizes the current insights into the adaptations of cardiomyocytes to mechanical stimuli and underlying mechanisms in ischemic heart disease, with focusing on mitochondrial metabolism. We also discuss how mechanical circulatory support (MCS) alters myocardial energy metabolism and affects the detrimental metabolic adaptations of the dysfunctional myocardium.

Havlenova T., Skaroupkova P., Miklovic M., Behounek M., Chmel M., Jarkovska D., Sviglerova J., Stengl M., Kolar M., Novotny J., Benes J., Cervenka L., Petrak J., Melenovsky V.

Mechanisms of right ventricular (RV) dysfunction in heart failure (HF) are poorly understood. RV response to volume overload (VO), a common contributing factor to HF, is rarely studied. The goal was to identify interventricular differences in response to chronic VO. Rats underwent aorto-caval fistula (ACF)/sham operation to induce VO. After 24 weeks, RV and left ventricular (LV) functions, gene expression and proteomics were studied. ACF led to biventricular dilatation, systolic dysfunction and hypertrophy affecting relatively more RV. Increased RV afterload contributed to larger RV stroke work increment compared to LV. Both ACF ventricles displayed upregulation of genes of myocardial stress and metabolism. Most proteins reacted to VO in a similar direction in both ventricles, yet the expression changes were more pronounced in RV (pslope: 

Kozhevnikova M.V., Belenkov Y.N., Shestakova K.M., Ageev A.A., Markin P.A., Kakotkina A.V., Korobkova E.O., Moskaleva N.E., Kuznetsov I.V., Khabarova N.V., Kukharenko A.V., Appolonova S.A.

Abstract Classifying heart failure (HF) by stages and ejection fraction (EF) remains a debated topic in cardiology. Metabolomic profiling (MP) offers a means to identify unique pathophysiological changes across different phenotypes, presenting a promising approach for the diagnosis and prognosis of HF, as well as for the development of targeted therapies. In our study, MP was performed on 408 HF patients (54.9% male). The mean ages of patients were 62 [53;68], 67 [65;74], 68 [61;72], and 69 [65;73] years for stages A, B, C, and D, respectively. This study demonstrates high accuracy in HF stage classification, distinguishing Stage A from Stage B with an AUC ROC of 0.91 and Stage B from Stage C with an AUC ROC of 0.97, by integrating chromatography-mass spectrometry data through multiparametric machine learning models. The observed metabolic similarities between HF with mildly reduced EF and HF with reduced EF phenotypes (AUC ROC 0.96) once again highlight the fundamental differences at the cellular and molecular levels between HF with preserved EF and HF with EF < 50%. Hierarchical clustering based on MP identified four distinct HF phenotypes and 26 key metabolites, including metabolites of tryptophan catabolism, glutamine, riboflavin, norepinephrine, serine, and long- and medium-chain acylcarnitines. The average follow-up period was 542.37 [16;1271] days. A downward change in the trajectory of EF [HR 3,008, 95% CI 1,035 to 8,743, p = 0,043] and metabolomic cluster 3 [HR 2,880; 95% CI 1,062 to 7,810, p = 0,0376] were associated with increased risk of all-cause mortality. MP can refine HF phenotyping and deepen the understanding of its underlying mechanisms. Metabolomic analysis illuminates the biochemical landscape of HF, aiding in its classification and suggesting new therapeutic pathways.

Ageev A.A., Kozhevnikova M.V., Tyurina D.A., Korobkova E.O., Kondratieva T.O., Shestakova K.M., Moskaleva N.E., Markin P.A., Khabarova N.V., Appolonova S.A., Belenkov Y.N.

Aim      To identify metabolomic and structure and function markers of remote left ventricular (LV) remodeling in patients with chronic heart failure (CHF) of ischemic etiology and LV ejection fraction (EF) <50%.Material and methods  This prospective study included 56 patients with 3-4 NYHA functional class CHF of ischemic etiology (mean age, 66±7 years) and 50 patients with ischemic heart disease (IHD) without signs of CHF (69 [64; 73.7] years). Concentration of 19 amino acids, 11 products of kynurenine catabolism of tryptophan, 30 acylcarnitines with different chain lengths were measured in all participants. The metabolites that showed statistical differences between the comparison groups were then used for the analysis. Echocardiography was used to assess LV cavity remodeling at the time of the CHF patient inclusion in the study and after 6 months of follow-up. Predictors of long-term LV cavity remodeling were assessed for this cohort taking into account statistically significant echocardiographic parameters and metabolites.Results Patients with CHF of ischemic etiology, predominantly (81%) had pathological calculated types of LV remodeling (concentric and eccentric hypertrophy, 46 and 35%, respectively). However, this classification had limitations in describing this cohort. In addition, in this group, the concentrations of alanine, proline, asparagine, glycine, arginine, histidine, lysine, valine, indolyl-3-acetic acid, indolyl-3-propionic acid, C16-1-OH, and C16-OH were significantly (p<0.05) lower, and the concentrations of most medium- and long-chain acylcarnitines were higher than in patients with IHD without signs of CHF. The long-term (6 months) reverse remodeling of the LV cavity in CHF of ischemic etiology was influenced by changes in the interventricular septum thickness (hazard ratio, HR, 19.07; 95% confidence interval, CI, 1.76-206.8; p=0.006) and concentrations of anthranilic acid (HR 19.8; 95% CI 1.01-387.8; p=0.019) and asparagine (HR 8.76; 95% CI 1.07-71.4; p=0.031).Conclusion      The presence of an interventricular septum thickness of more than 13.5 mm, anthranilic acid concentrations of higher than 0.235 μM/l, or an asparagine concentration of less than 135.2 μM/l in patients with CHF of ischemic etiology after 6 months of follow-up affects their achievement of LV cavity reverse remodeling. 

Kozhevnikova M.V., Belenkov Y.N., Shestakova K.M., Ageev A.A., Markin P.A., Kakotkina A.V., Korobkova E.O., Moskaleva N.E., Kuznetsov I.V., Khabarova N.V., Kukharenko A.V., Appolonova S.A.

AbstractBackgroundThe existing classifications of heart failure (HF) remain a topic of debate within the cardiology community. Metabolomic profiling (MP) offers a means to define HF phenotypes based on pathophysiological changes, allowing for more precise characterization of patient groups with similar clinical profiles. MP may thus aid in refining HF classifications and offer a novel approach to phenotyping.MethodsMP was performed to 408 patients with different stages of HF. Patients with symptomatic HF were divided into phenotypes by left ventricle ejection fraction (LVEF). Liquid chromatography combined with mass-spectrometry were used for the MP. Data were analyzed using machine learning. The relationship between the incidence of all-cause death and LVEF trajectory changes and metabolomic clusters was evaluated. Follow-up period was 542 days [16;1271] in average.ResultsThe classification model achieved an AUC ROC - 0.91 for distinguishing of Stage A from Stage B and an AUC ROC - 0,97 for Stage B vs. Stage C using metabolomic analysis, model’s performance for differentiating Stages C and D was lower (AUC ROC 0.81). For HF phenotypes, the HFrEF, HFmrEF, and HFpEF model demonstrated moderate accuracy (AUC ROC 0.74), whereas the model distinguishing HFpEF from HF with EF <50% showed good precision. The HFrEF vs. HF with EF >40% model, however, displayed low accuracy. Biostatistical processing of MP identified four metabolomic clusters, and 26 metabolites demonstrated the greatest significance (metabolites of the kynurenine and serotonin pathways of tryptophan catabolism, glutamine, riboflavin, norepinephrine, serine, long- and medium-chain acylcarnitines). Patients with reduced LVEF had the poorest prognosis (HR 1,896; 0,711–5,059), with an LVEF decrease linked to a threefold rise in all-cause mortality risk. Cluster 3 was associated with a 2,880-fold increase in all-cause mortality.ConclusionsOur findings suggest that MP provides an effective alternative approach for stratifying HF patients by stage. The observed metabolic similarities between HFpEF and HFrEF phenotypes highlight limitations in the current classification, underscoring the need to refine HF phenotyping into two primary categories. Hierarchical clustering by metabolomic profile produced a high-accuracy model, supporting MP as a valuable tool for HF classification.Novelty and SignificanceWhat Is Known?Classifying HF by stages offers the advantage of incorporating preventive aspects, however, diagnosing Stage B can be challenging due to the necessity of analyzing numerous parameters for verification.The classification by LVEF, particularly distinguishing HFmrEF, remains controversial because of limited evidence for specific treatment strategies.Metabolomic profiling (MP) offers a means to identify unique pathophysiological changes across phenotypes, presenting a promising approach for diagnosing HF and developing targeted therapies.What New Information Does This Article Contribute?This study demonstrates high accuracy in HF stage classification by integrating chromatography-mass spectrometry data with bioinformatic analysis through multiparametric machine learning (ML) models.Similarities between metabolomic profiles of patients with LVEF <40% and those with LVEF 41–49% suggest pathophysiological overlap between HFmrEF and HFrEF.MP, enhanced by ML, allows precise differentiation between HFpEF and patients with EF <50%, with an AUC ROC of 0.96.Hierarchical clustering based on metabolomic profiles identified four distinct HF phenotypes, reflecting unique pathophysiological pathways with high accuracy (AUC ROC 0.96).This work highlights how metabolomic analysis provides insight into HF’s biochemical landscape, aiding classification and offering potential new pathways for therapeutic intervention. The findings underscore MP’s potential to improve HF phenotyping and understanding of disease mechanisms.Graphical abstractEF – ejection fraction; HF – heart failure; HFmrEF - heart failure with mid-range ejection fraction; HFpEF - heart failure with preserved ejection fraction; HFrEF - heart failure with reduced ejection fraction.