Skip to main content
NCBI home page
Springer logoLink to Springer
. 2025 Aug 29;21(5):130. doi: 10.1007/s11306-025-02317-0

Plasma non-targeted metabolomics unravels the metabolic features of normal trans-right heart

Xifeng Qian 1, Yuanrui Deng 1, Tingting Guo 1, Xin Huang 2, Chaowu Yan 3, Xin Gao 1, Yan Wu 1, Xinxin Yan 1, Zhiqiang Liu 1, Song Hu 1, Jiangshan Tan 1, Lingtao Chong 1, Shengsong Zhu 1, Mingjie Ma 4, Mengting Ye 4, Lu Hua 1,5,✉,#, Jian Cao 2,#, Xiaojian Wang 4,#
PMCID: PMC12397194  PMID: 40879874

Abstract

Introduction

Right heart (RH), as a junction between the venous system and pulmonary circulation, gains great emphasis on exploring the relevant pathological mechanism of many cardiopulmonary diseases. Although these pathogensis researches centering on RH-related diseases advance, the physiological mechanism research of the RH is scarce.

Objectives

This study aimed to accurately unravel the metabolic features of normal trans-RH through non-targeted metabolomics.

Methods

Patent foramen ovale (PFO) participants with normal function of RH were recruited and their blood samples from superior vena cava (SVC) and pulmonary artery (PA) were collected through right cardiac catheterization. Non-targeted metabolomics analysis based on UHPLC-MS/MS was utilized to generate the metabolic feature of trans-RH by comparing the metabolites change from SVC to PA, revealing its physiological gradient metabolic mechanism.

Results

1060 metabolites were tentatively identified in blood samples from 28 PFO participants. 51 differential metabolites were defined based on screening criteria after flowing through RH, including 39 down-regulated metabolites and 12 up-regulated metabolites. Among them, phosphatidylcholines, sphingomyelins, amino acids, triacylglycerol, neopterin, and tetradecanedioic acid were the most relevant.

Conclusion

Our study provides a more profound and extensive understanding of the psychological metabolism of trans-RH, expanding the current knowledge of normal RH function and providing clues for the pathogenesis research of RH-related diseases.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11306-025-02317-0.

Keywords: Right heart, Metabolomics, Physiological metabolism, Damage and repair

Introduction

Right heart (RH) is responsible for receiving vena cava blood and delivering it to the lungs, whose dysfunction is currently reckoned as a pivotal prognostic factor in various cardiopulmonary diseases (O’Donnell et al., 2023). RH was historically regarded as a dispensable component of the circulatory system that may be passed in certain complex congenital cardiac conditions. However, as research advances, the extensive significance of RH malfunction has gained great emphasis on myocardial infarction, heart failure, pulmonary embolism, and pulmonary hypertension (Haddad et al., 2008; Voelkel et al., 2006; Barco et al., 2019). Although several studies have investigated the crucial role of RH in the above diseases, its pathophysiological characteristics have not been totally elucidated and further exploration is needed.

Metabolomics has become a crucial technique that allows us to comprehensively assess the metabolic framework within the body, revealing an organism’s endophenotype state (Ajoolabady et al., 2024). Several diseases have showcased the abnormal metabolic alterations, such as RH-related diseases. Preclinical studies indicated that significant metabolic changes and metabolic reprogramming were identified in animal right ventricular tissues of pulmonary arterial hypertension through utilizing metabolomics analysis (Talati et al., 2016; Graham et al., 2018). Additionally, a clinical study further demonstrated that distinct plasma metabolic profiles were linked to right ventricular dilation in patients with pulmonary arterial hypertension (Pi et al., 2023). Current metabolomics investigations on RH were done under disease conditions; on normal RH, however, is lacking. The study of gradient metabolism in normal trans-RH is also lacking. Gradient metabolic changes can better show the functional status of an organ without the disturbances of other organs. Collecting blood samples from peripheral arteries to perform metabolomics analysis can not fully represent the metabolism of RH due to the interference of tissue metabolism in peripheral organs. Analysis of RH in-situ samples can accurately reveal the gradient metabolism of RH without the influence of other organs. Nevertheless, the infeasibility of sample collection of RH from healthy people further hampers exploring the metabolic profile of RH because they will not agree the protocol of sample collection in situ without any indication. Patent foramen ovale (PFO) is the most frequent congenital heart abnormality originating during fetal development. Although PFO prevalence correlates with several extra cardiac pathological conditions such as stroke and migraine headache (Homma et al., 2016), PFO has no great influence on the function and structure of the heart itself, only slight shunt affecting hemodynamics. Thus, PFO is a favorable surrogate for studying the physiological processes of the heart when direct RH samples from healthy people are not available.

Understanding the physiological metabolism of RH is the cornerstone to comprehend its related pathological mechanism. To bridge the research gap, our study enrolled PFO participants with normal structure and function of RH and collected blood samples from superior vena cava (SVC) and pulmonary artery (PA) when PFO closure was performed through right cardiac catheterization. Non-targeted metabolomics analysis was employed to generate the gradient metabolic feature of trans-RH, revealing its physiological metabolic mechanism (cardiac blood flow pattern diagram and general study protocol shown in Fig. 1).

Fig. 1.

Fig. 1

Open in a new tab

Participants enrollment and study protocol. A A cardiac sectional illustration of showing the blood flow. The white lines with arrow represent the blood flow; The yellow dots indicate the anatomical site where the blood samples were collected. B A flowchart of participants recruitment. C Cartoon illustration of the study protocol (Color figure online)

Methods

Participant selection

This study obtained the approval of the Institutional Review Boards of Fuwai hospital (Beijing, China), following the International Conference on Harmonization guidelines and the Helsinki Declaration. The inclusion criteria comprised participants aged 18–70 years, a definite diagnosis of PFO with normal function of RH confirmed by echocardiography, and integrated clinical information. The normal function of RH was defined: tricuspid annular plane systolic excursion (TAPSE), normal (Lang et al., 2015); left ventricular ejection fraction (LVEF), 53-73% (Lang et al., 2015); N-terminal pro-brain natriuretic peptide (NT-proBNP), < 125 pg/ml (Bansal et al., 2022). The exclusion criteria included acute infections, severe hepatic and renal impairment, malignant tumors, severe mental diseases and other diseases that affect metabolism. After signing the informed consent, eligible participants were enrolled in this study.

Sample collection and processing

SVC and PA samples were collected from each included participant when PFO closure was carried out via right cardiac catheterization. After inserting the catheter into the pulmonary trunk and SVC, respectively, the initial 1 ml of blood was disposed of, then 10 ml of blood was drawn into a 3.2% sodium citrate tube. These tubes were delivered to the lab within 1 h after being promptly stored in an ice box at 4 °C. After the first centrifugation at 4℃, 200 g, 10 min, up rate 9 and down rate 5, the upper plasma was extracted for second centrifugation at 4℃, 2000 g, 10 min, up rate 9 and down rate 9. Lastly, the plasma was collected into a 2 ml freezing tube, and then rapidly frozen in liquid nitrogen at -80 °C until analysis.

Non-targeted metabolomics analysis

Metabolites extraction

100 µl of plasma was placed in an eppendorf tube and resuspended with pre-cooled 80% methanol by well vortex. After a 5-min incubation on ice, the sample was centrifuged at 15,000 g for 20 min at 4℃. The supernatant was then diluted with Liquid Chromatograpghy-Mass Spectrometer (LC-MS) grade water to reach a final concentration of 53% methanol, moved to a fresh eppendorf tube and centrifuged once more under the same conditions. Ultimately, the supernatant was introduced into Ultra High Performance Liquid Chromatograpghy-Mass Spectrometer/Mass Spectrometer (UHPLC-MS/MS) system (Barri & Dragsted, 2013). To control the quality of the experiment, Quality control (QC) samples were prepared while processing the samples. QC sample was an equal volume mixed sample of experimental samples, which was used to balance the LC-MS/MS system and monitor the instrument state, and evaluate the stability of the system during the whole experiment. Meanwhile, blank samples (53% methanol aqueous solution) were set, which was mainly used to remove background ions.

UHPLC-MS/MS analysis

The samples were analyzed using a Vanquish UHPLC system in conjunction with an Orbitrap Q Exactive™ HF-X mass spectrometer in Novogene Co., Ltd (Beijing, China). The samples were introduced into a Hypersil Gold column (100*2.1 mm, 1.9 μm) at a flow rate of 0.2 mL/min and the temperature of 40℃. The eluents included 5 mM ammonium acetate at pH 9.0 (eluent A) and methanol (eluent B) for the negative polarity mode while 0.1% formic acid in water (eluent A) and methanol (eluent B) for the positive polarity mode. The linear gradient program was as follows: 2% B for 0 min; 2% B for 1.5 min; 85% B for 3 min; 100% B for 10 min; 2% B for 10.1 min; 2% B for 12 min. The Q Exactive™ HF-X mass spectrometer was worked in both positive and negative polarity modes with the following settings of electrospray ionization source: 3.5 kV spray voltage, 320℃ capillary temperature, 35 psi sheath gas flow rate, 10 l/min auxiliary gas flow rate, 60 S-lens RF level, and 350℃ auxiliary gas heater temperature. Data-dependent acquisition was employed during mass spectrometric analysis. MS1 parameters were as follows: a scanning range of m/z of 100–1500, an automatic gain control target of 3e6, a maximum injection time of 100ms, and a resolution of 60,000. MS2 settings were as follows: an automatic gain control target of 2e5, a maximum injection time of 25ms, a resolution of 15,000, and the normalized collision energies of 20, 40 and 60 eV.

Data processing and metabolite identification

Compound Discoverer 3.1 was used to process the raw data file from the LC-MS/MS analysis to obtain the qualitative and quantitative metabolites. The key setting parameters included retention time tolerance of 0.2 min, actual mass tolerance of 5ppm, signal intensity tolerance of 30%, signal-to-noise ratio of 3, minimum signal intensity, additive ions for peak alignment, picking and quantitation. Subsequently, peak intensities was standardized to the total spectral intensity. These normalized data served as the basis for molecule formula according to molecular ion peaks, additive ions, and fragment ions, and then peaks were matched with the three reference databases Masslist, mzVault, and MZCloud (https://www.mZCloud.org/). Only compounds with < 30% coefficient of variance in QC sample peak areas were retained. Additionally, one annotated feature corresponding one metabolite with highest possibility were included based on the above conditions. Ultimately, after removing background ions and normalizing for the original results, the qualitative and semi-quantitative metabolites were obtained. R language (version 3.4.3), Python (version 2.7.6) and CentOS (version 6.6) were used for data analyses. If the data was not normally distributed, the normal transformation would be carried out using the area normalization approach.

Statistical analyses

These metabolites were annotated using KEGG (https://www.genome.jp/kegg/pathway.html), HMDB (https://hmdb.ca/metabolite) and LIPIDMaps databases(http://www.lipidmaps.org/). Firstly, QC analysis was conducted by calculating pearson correlation coefficient between QC samples based on the relative quantitative values of metabolites. The correlation heatmap showed that all R2 values exceeded 0.95 in positive ion mode while 0.98 in negative ion mode, indicating good experimental stability and high-quality data. Subsequently, QC samples were excluded for further analysis. In multivariate analysis, the supervised-based partial least squares discriminant analysis (PLSDA) model was carried out at metaX to observe whether there was a clear separation between PA and SVC samples(Wen et al., 2017). Besides, Variable Importance in Projection (VIP) score calculated from PLSDA was considered as one of cut-off values to identify the differential metabolites. For model quality, 7-fold cross-validation and 200-time permutation tests were employed, with evaluation of four parameters (R2Y, Q2Y, R2, and Q2). In univariate analysis, metabolites were statistically evaluated using a T test to calculate fold changes (FC) and determine significance (P value) between groups. P value was adjusted for multiple testing corrections to control false discovery rate by using Benjamini-Hochberg method. The criteria for identification of differential metabolites were FC > 1.5 or FC < 0.67, adjusted P value < 0.05, and VIP > 1. The correlation and clustering analysis between differential metabolites was performed (analytical approach of pearson) and drawn by Omicshare tool (https://www.omicshare.com/).

Results

Study participants

43 PFO participants were originally recruited in this present study. According to the inclusion and exclusion criteria, 28 participants were ultimately eligible to include. Complete clinical data of each participant, including gender, age, height, weight, NT-proBNP, D-dimer, echocardiographic data, and diagnosis, was listed in Table S1. There were 7 males and 21 females in these participants, with a mean age of 41.25 ± 9.76 years old. We could conclude that participants’ cardiac function was normal as LVEF was not more than 73% or less than 53%, TAPSE was normal, and NT-proBNP was less than 125 pg/ml in all participants. Additionally, 21 participants had symptoms of migraine. A total of 56 blood samples were collected for non-targeted metabolomics analysis.

PLSDA outcomes

PLSDA model comparing the different groups was established, as shown in Fig. 2A. The result showed that the metabolic spectrum distribution were clearly distinct between two groups. Cross validation showed that R2Y (0.97) and Q2Y (0.81) are close to 1, indicating that the model is stable and reliable. The plot of PLSDA permutation tests demonstrated that R2 data is greater than Q2 data and the intercept between Q2 regression line and Y axis is less than 0 (Fig. 2B), indicating the no over-fitting of the model. The obtained model consolidated the initial thought that there was a significant difference between two groups.

Fig. 2.

Fig. 2

Open in a new tab

Plots of PLSDA model (A) and PLSDA permutation tests (B)

Identification of differential metabolites

After setting parameters for peak alignment, picking, quantification and standard database comparison for each metabolite, a total of 1060 unique metabolites corresponding one annotated feature with highest possibility were tentatively identified (Table S2). In short, amino acid-related metabolites accounted for the most (146 metabolites, 13.8%), including amino acids (24), their derivatives (96), and peptides (25). Then, heterocyclics (93, 8.8%), fatty acids (80, 7.5%), aromatics(64, 6.0%), glycerophospholipids (58, 5.5%), lysophospholipids (43, 4.1%), arachidonic acids (42, 4.0%), nucleotides (39, 3.7%), steroids (35, 3.3%) followed in order. Subsequently, a total of 51 differential metabolites were defined based on screening criteria. The pertinent metabolite data and related parameters in detail were showcased in Fig. 3 and Table S3. Among these, when blood flowed through RH, there were 39 metabolites at lower levels and 12 metabolites at higher levels. In general, there were some metabolites classified into the same category, including heterocyclics (6 metabolites), sphingolipids (5), glycerophospholipids (5), amino acids and their derivatives (4), alkaloids (3), amines (3), arommatics (3), carboxylic acids (3), and flavonoids (3). Specifically, as can be seen from Fig. 3, several phosphatidylcholines changed to varying degrees passing through RH with 3 up-regulated metabolites (PC(5:0/16:4), PC(18:2e/16:0), and PC(17:0/18:2)) and 2 down-regulated metabolites (PC(18:3/20:4) and PC(18:1e/21:1)). The levels of 4 sphingomyelins (SM (d16:2/26:0), SM (d26:3/18:2), SM (d17:1/25:1), and SM (d17:2/25:0)) were lower at PA than at SVC, while 1 (SM (d14:0/26:1)) was higher. Additionally, we also found amino acids, triacylglycerol, and neopterin decreased while tetradecanedioic acid increased when flowing through RH. The relationship and clustering between all divergent metabolites was analyzed to reveal the co-regulation between metabolites. As displayed in Fig. 4 and Table S4, there were a total of 96 pairs of metabolites with adjusted P value of < 0.01. Among them, the relatioship between phosphatidylcholines, sphingomyelins, and between phosphatidylcholines and sphingomyelins account for a relative large proportion (14 pairs), indicating that they may interact to jointly maintain the metabolic homeostasis of RH.

Fig. 3.

Fig. 3

Open in a new tab

Dotrod heatmap of all 51 differential metabolites ranked by VIP value

Fig. 4.

Fig. 4

Open in a new tab

Correlation and clustering heatmap of all differential metabolites

Discussion

To our knowledge, this study firstly investigated the metabolic outline of RH system. Based on 56 blood samples, our study found that there were a total of 51 differential metabolites identified, including 39 down-regulated metabolites and 12 up-regulated metabolites after flowing through RH. Concretely, phosphatidylcholines, sphingomyelins, amino acids, triacylglycerol, and neopterin were identified. Besides, in the change of metabolite gradient, phosphatidylcholines and sphingomyelins interact to jointly affect the metabolic homeostasis of RH.

The potential mechanisms of the above-mentioned metabolites with gradient change in RH perhaps involve several physiological processes, including cell proliferation and survival, energy storage and consumption, cell signal transduction. Phosphatidylcholine, a crucial part of lipid compounds, is involved in diverse physiological processes, such as glycerophospholipid metabolism, energy storage, cell signal transduction, and membrane integrity and stability (Ridgway, 2013, Furse and de Kroon, 2015). It can participate in several physiological processes and its dysregulation involves pathological mechanism in the process of disease occurrence and development. It is reported that phosphatidylcholine is closely associated with atherosclerosis, as lipidomic profiling has reported that the serum levels of 3 phosphatidylcholines are considerably altered in patients with severe calcific coronary artery disease than patients without this condition, which may involve inflammatory process (Djekic et al., 2019). Additionally, 19 phosphatidylcholines were significantly down-regulated in the conditions from myocardial infarction (MI) to post-MI heart failure, which may play a role in the development of lipotoxic cardiomyopathy, impaired mitochondrial function, and inflammation in heart failure patients and subsequent MI-related lipotoxic cardiomyopathy (Rong et al., 2023). Our study revealed that 5 differential phosphatidylcholines were changed to varying degrees after flowing RH This is perhaps associated with the maintenance of myocardial lipid metabolic homeostasis and mitochondrial function, and avoidance of inflammation, indicating that significantly content altering of some certain phosphatidylcholines above-mentioned may herald the dysfunction of physiological lipid metabolism in RH. Sphingomyelin is the part of the fatty acid family of sphingolipids, which are essential for the structural integrity of cell membranes (Kraft, 2016). Sphingomyelin in the plasma membrane directly influences cholesterol homeostasis, especially, affecting cholesterol biosynthesis, LDL binding to cell surface receptors and subsequent internalization (Kikas et al., 2018). Additionally, sphingomyelin can be the source of diverse lipid signaling molecules through the catalyzation of its acidic or neutral synthases to response various biological stimuli, whose metabolites, including ceramide, sphingosine, and sphingosine 1-phosphate, are of greater significance in cell proliferation, differentiation and apoptosis (Kikas et al., 2018). They are intimately connected to the development and progress of cardiovascular diseases, including atherosclerosis (Bojic et al., 2014), heart failure (Lemaitre et al., 2019) and right cardiac dysfunction of pulmonary arterial hypertension (Pi et al., 2023) by functioning in oxidative stress, endothelial injury, inflammation and lipotoxicity. Similarly, our study also found that the levels of 5 sphingomyelins were significantly shifting after passing through RH. From the above discussion, we speculate that sphingomyelins with significant alteration in our study may be involved in the structural integrity of cell membrane, cholesterol homeostasis, and signal transduction pathway. Besides, we found that phosphatidylcholines and sphingomyelins perhaps interact to jointly affect the metabolic homeostasis of RH. The reason may be that they share with similar physiological functions according to the above statement, such as lipid metabolic homeostasis.

The metabolic change of amino acids was also recognized in RH. The levels of alanyltyrosine and glutamic acid were lower at PA than at SVC, indicating that these amino acids may be utilized to generate anti-oxidant materials against various oxidative stresses in RH metabolism (Qi et al., 2013). Besides, we found a reduction of a triacylglycerol, suggesting that it was probably applied to produce energy for myocardial cells’ metabolism and cardiolipotoxicity could thus be avoided. Another reduced metabolite was neopterin, known as an inflammatory biomarker that is released to respond to macrophage activation, and a study confirmed that elevated neopterin levels were associated with the development and progression of heart failure (Dogheim et al., 2022). Furthermore, neopterin involves folate biosynthesis process, interlinking methionine metabolism to produce methyl groups that are vital for numerous essential processes such as DNA synthesis, antioxidant production, and amino acid balance (Lyon et al., 2020). Thus, we deduce that its reduction in our study is related to nucleic acid synthesis, antioxidation, and amino acid metabolism in the heart tissue. N-formylkynurenine, a crucial metabolite in the tryptophan metabolic pathways, was at a lower level after crossing the RH. This metabolite is generated through reactive oxygen species (ROS)-mediated tryptophan degradation (Carroll et al., 2018). The observed reduction in N-formylkynurenine levels across the physiological RH circulation suggests limited ROS activity in this normal cardiac environment.

As a transit point connecting the venous system and pulmonary circulation, RH always faces the influence of venous blood on the endocardial wall. The impact of venous blood pressure and the chemical effects of various components produced by body metabolism in venous blood on the endocardial wall drive RH to make some changes to adapt to it. The results of our study give some instructive explanations regarding what changes have been made to RH. Under endogenous and exogenous stimuli, the processes of injury and subsequent repair persist throughout the lifespan in RH to maintain the internal milieu homeostasis. Phosphatidylcholines and sphingomyelins alteration mirror in response to normal or abnormal stimuli through diverse processes such as cell proliferation and survival, energy storage and consumption, cell signaling transduction, and antioxidant injury. The change of amino acids and tryptophan metabolite also found to fight against oxidative stress. What’s more, folate biosynthesis plays the key role in the repair process by nucleic acid synthesis and antioxidant production.

However, this study has several limitations when interpreting our outcomes. First, blood samples in RH obtained from PFO participants do not fully represent the samples in RH from healthy individuals, hindering the generalization of our results to all. Second, the majority of enrollees suffered from migraine, which perhaps contributed to the metabolic change in trans-RH. Despite these conditions, migraine is a neurological disease mainly acting on the brain but may not be related to the heart function (Domitrz et al., 2014). Third, the sample size was relatively small (only 28), reducing the persuasiveness of our results. Fourth, all metabolite annotations are tentatively identified, and an target verification for these 51 differential metabolites is needed by using targeted metabolomics in the future.

To sum up, our study provides a more profound and extensive understanding of the psychological metabolism of trans-RH, revealing significant gradient changes in 51 metabolites. These findings expand the current knowledge of normal RH function and provide clues for the pathogenesis research of RH-related diseases. Further study is needed to explore the concrete mechanism of these differential metabolites for maintaining psychological RH’s function, potentially informing therapeutic strategies for cardiopulmonary diseases.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (740KB, xls)

Abbreviations

RH

Right heart

PFO

Patent foramen ovale

SVC

Superior vena cava

PA

Pulmonary artery

PLSDA

Partial least squares discriminant analysis

VIP

Variable importance in projection

UHPLC-MS/MS

Ultra high performance liquid chromatograpghy-mass spectrometer/mass spectrometer

QC

Quality control

FC

Fold changes

Author contributions

X.Q., Y.D., T.G., L.H., and J.C. proposed the idea and designed the study. X.Q., Y.D., Y.W., X.Y., screened PFO patients. T.G., X.H., C.Y., X.G., Z.L., S.H., L.C., and S.Z. made heart catheterization and collected the SVC and PA samples. Y.D., X.H., J.T., M.M., M.Y., and X.W. performed sample processed and stored. X.Q. and Y.D performed the data analysis. X.Q. drafted and revised the manuscript. L.H., J.C., and X.W. revised the manuscript.

Funding

This work was supported by the Research Project of the National High Level Hospital Clinical Research Funding (Grant No 2022-GSP-QN- 4), Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences(Grant No 2022-I2M-C&T-B-040), and National Clinical Research Center of Cardiovascular Diseases, Shenzhen. Fuwai Hospital Chinese Academy of Medical Sciences, Shenzhen (Grant No NCRCSZ-2023-015).

Data availability

The raw data came from the included participants who signed the informed consent.

Declarations

Conflict of interest

The authors declare no competing interests.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Lu Hua, Jian Cao and Xiaojian Wang contributed equally to the study and are joint corresponding authors.

References

  1. Ajoolabady, A., Pratico, D., Dunn, W. B., Lip, G. Y. H., & Ren, J. (2024). Metabolomics: Implication in cardiovascular research and diseases. Obesity Reviews, e13825.
  2. Bansal, N., Zelnick, L. R., Ballantyne, C. M., Chaves, P. H. M., Christenson, R. H., Coresh, J., Defilippi, C. R., De Lemos, J. A., Daniels, L. B., Go, A. S., He, J., Hedayati, S. S., Matsushita, K., Nambi, V., Shlipak, M. G., Taliercio, J. J., & Seliger, S. L. (2022). Upper reference limits for high-sensitivity cardiac troponin T and N-terminal fragment of the prohormone brain natriuretic peptide in patients with CKD. American Journal of Kidney Diseases, 79, 383–392. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Barco, S., Mahmoudpour, S. H., Planquette, B., Sanchez, O., Konstantinides, S. V., & Meyer, G. (2019). Prognostic value of right ventricular dysfunction or elevated cardiac biomarkers in patients with low-risk pulmonary embolism: A systematic review and meta-analysis. European Heart Journal, 40, 902–910. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Barri, T., & Dragsted, L. O. (2013). UPLC-ESI-QTOF/MS and multivariate data analysis for blood plasma and serum metabolomics: Effect of experimental artefacts and anticoagulant. Analytica Chimica Acta, 768, 118–128. [DOI] [PubMed] [Google Scholar]
  5. Bojic, L. A., Mclaren, D. G., Shah, V., Previs, S. F., Johns, D. G., & Castro-perez, J. M. (2014). Lipidome of atherosclerotic plaques from hypercholesterolemic rabbits. International Journal of Molecular Sciences, 15, 23283–23293. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Carroll, L., Pattison, D. I., Davies, J. B., Anderson, R. F., Lopez-alarcon, C., & Davies, M. J. (2018). Superoxide radicals React with peptide-derived tryptophan radicals with very high rate constants to give hydroperoxides as major products. Free Radical Biology and Medicine, 118, 126–136. [DOI] [PubMed] [Google Scholar]
  7. Djekic, D., Pinto, R., Repsilber, D., Hyotylainen, T., & Henein, M. (2019). Serum untargeted lipidomic profiling reveals dysfunction of phospholipid metabolism in subclinical coronary artery disease. Vascular Health and Risk Management, 15, 123–135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Dogheim, G. M., Amralla, M. T., & Werida, R. H. (2022). Role of neopterin as an inflammatory biomarker in congestive heart failure with insights on effect of drug therapies on its level. Inflammopharmacology, 30, 1617–1622. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Domitrz, I., Styczynski, G., Wilczko, J., Marczewska, M. M., Domitrz, W., & Kaminska, A. (2014). An association between migraines and heart anomalies-true or false? A heart ultrasound study using cTTE in migraine patients and control participants. Pain Medicine (Malden, Mass), 15, 2156–2160. [DOI] [PubMed] [Google Scholar]
  10. Furse, S., & De Kroon, A. I. (2015). Phosphatidylcholine’s functions beyond that of a membrane brick. Molecular Membrane Biology, 32, 117–119. [DOI] [PubMed] [Google Scholar]
  11. Graham, B. B., Kumar, R., Mickael, C., Kassa, B., Koyanagi, D., Sanders, L., Zhang, L., Perez, M., Hernandez-saavedra, D., Valencia, C., Dixon, K., Harral, J., Loomis, Z., Irwin, D., Nemkov, T., D’alessandro, A., Stenmark, K. R., & Tuder, R. M. (2018). Vascular adaptation of the right ventricle in experimental pulmonary hypertension. American Journal of Respiratory Cell and Molecular Biology, 59, 479–489. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Haddad, F., Hunt, S. A., Rosenthal, D. N., & Murphy, D. J. (2008). Right ventricular function in cardiovascular disease, part I: Anatomy, physiology, aging, and functional assessment of the right ventricle. Circulation, 117, 1436–1448. [DOI] [PubMed] [Google Scholar]
  13. Homma, S., Messé, S. R., Rundek, T., Sun, Y. P., Franke, J., Davidson, K., Sievert, H., Sacco, R. L., & Di Tullio, M. R. (2016). Patent foramen ovale. Nature Reviews Disease Primers, 2, 15086. [DOI] [PubMed] [Google Scholar]
  14. Kikas, P., Chalikias, G., & Tziakas, D. (2018). Cardiovascular implications of sphingomyelin presence in biological membranes. European Cardiology Review, 13, 42–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Kraft, M. L. (2016). Sphingolipid organization in the plasma membrane and the mechanisms that influence it. Frontiers in Cell and Developmental Biology, 4, 154. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Lang, R. M., Badano, L. P., Mor-avi, V., Afilalo, J., Armstrong, A., Ernande, L., Flachskampf, F. A., Foster, E., Goldstein, S. A., Kuznetsova, T., Lancellotti, P., Muraru, D., Picard, M. H., Rietzschel, E. R., Rudski, L., Spencer, K. T., Tsang, W., & Voigt, J. U. (2015). Recommendations for cardiac chamber quantification by echocardiography in adults: An update from the American society of echocardiography and the European association of cardiovascular imaging. Journal of the American Society of Echocardiography, 28, 1–39e14. [DOI] [PubMed] [Google Scholar]
  17. Lemaitre, R. N., Jensen, P. N., Hoofnagle, A., Mcknight, B., Fretts, A. M., King, I. B., Siscovick, D. S., Psaty, B. M., Heckbert, S. R., Mozaffarian, D., & Sotoodehnia, N. (2019). Plasma ceramides and sphingomyelins in relation to heart failure risk. Circulation. Heart Failure, 12, e005708. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Lyon, P., Strippoli, V., Fang, B., & Cimmino, L. (2020). B vitamins and one-carbon metabolism: Implications in human health and disease. Nutrients, 12.
  19. O’Donnell, C., Sanchez, P. A., Celestin, B., Mcconnell, M. V., & Haddad, F. (2023). The echocardiographic evaluation of the right heart: Current and future advances. Current Cardiology Reports, 25, 1883–1896. [DOI] [PubMed] [Google Scholar]
  20. Pi, H., Xia, L., Ralph, D. D., Rayner, S. G., Shojaie, A., Leary, P. J., & Gharib, S. A. (2023). Metabolomic signatures associated with pulmonary arterial hypertension outcomes. Circulation Research, 132, 254–266. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Qi, L., Qi, Q., Prudente, S., Mendonca, C., Andreozzi, F., Di Pietro, N., Sturma, M., Novelli, V., Mannino, G. C., Formoso, G., Gervino, E. V., Hauser, T. H., Muehlschlegel, J. D., Niewczas, M. A., Krolewski, A. S., Biolo, G., Pandolfi, A., Rimm, E., Sesti, G., Trischitta, V., Hu, F., & Doria, A. (2013). Association between a genetic variant related to glutamic acid metabolism and coronary heart disease in individuals with type 2 diabetes. Jama, 310, 821–828. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Ridgway, N. D. (2013). The role of phosphatidylcholine and choline metabolites to cell proliferation and survival. Critical Reviews in Biochemistry and Molecular Biology, 48, 20–38. [DOI] [PubMed] [Google Scholar]
  23. Rong, J., He, T., Zhang, J., Bai, Z., & Shi, B. (2023). Serum lipidomics reveals phosphatidylethanolamine and phosphatidylcholine disorders in patients with myocardial infarction and post-myocardial infarction-heart failure. Lipids in Health and Disease, 22, 66. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Talati, M. H., Brittain, E. L., Fessel, J. P., Penner, N., Atkinson, J., Funke, M., Grueter, C., Jerome, W. G., Freeman, M., Newman, J. H., West, J., & Hemnes, A. R. (2016). Mechanisms of lipid accumulation in the bone morphogenetic protein receptor type 2 mutant right ventricle. American Journal of Respiratory and Critical Care Medicine, 194, 719–728. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Voelkel, N. F., Quaife, R. A., Leinwand, L. A., Barst, R. J., Mcgoon, M. D., Meldrum, D. R., Dupuis, J., Long, C. S., Rubin, L. J., Smart, F. W., Suzuki, Y. J., Gladwin, M., Denholm, E. M., & Gail, D. B. (2006). Right ventricular function and failure: Report of a national heart, lung, and blood Institute working group on cellular and molecular mechanisms of right heart failure. Circulation, 114, 1883–1891. [DOI] [PubMed] [Google Scholar]
  26. Wen, B., Mei, Z., Zeng, C., & Liu, S. (2017). MetaX: A flexible and comprehensive software for processing metabolomics data. Bmc Bioinformatics, 18, 183. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (740KB, xls)

Data Availability Statement

The raw data came from the included participants who signed the informed consent.

Articles from Metabolomics are provided here courtesy of Springer

RESOURCES