Biomarker identification and risk assessment of cardiovascular disease based on untargeted metabolomics and machine learning.
Cardiovascular disease
Ischemic stroke
Machine learning
Metabolite annotation
Risk assessment
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
28 10 2024
28 10 2024
Historique:
received:
18
03
2024
accepted:
22
10
2024
medline:
29
10
2024
pubmed:
29
10
2024
entrez:
29
10
2024
Statut:
epublish
Résumé
Cardiovascular disease (CVD) is the leading cause of mortality, disability, and healthcare costs, with a significant impact on the elderly and contributing to premature deaths across various age groups, including those below age 70. Despite decades of transformative discoveries and clinical efforts, the challenges of diagnosis, prevention, and treatment of CVD persist on a massive scale. This study aimed to unravel potential CVD-associated biomarkers and establish a machine learning model for the risk assessment of CVD. Untargeted metabolic assay with ultra-high performance liquid chromatography-tandem mass spectrometry and routine clinical biochemistry test were undertaken on the fasting venous blood specimens from 57 subjects. Four relevant clinical traits and 164 CVD-associated metabolites were identified, especially those related to glycerophospholipid metabolism and biosynthesis of unsaturated fatty acids. The machine learning model achieved from an integrated biomarker panel of palmitic amide, oleic acid, 138-pos (the 138th detected metabolomic feature in positive ion mode), phosphatidylcholine, linoleic acid, age, direct bilirubin, and inorganic phosphate, was able to improve the accuracy of CVD risk assessment up to a high satisfactory value of 0.91. The findings indicate that disorders in the metabolic processes of biological membranes and energy are significantly associated with increased risk of vascular damage in CVD patients. With machine learning methods, the pivotal metabolites and clinical biomarkers offer a promising potential for the efficient risk assessment and diagnosis of CVD.
Identifiants
pubmed: 39468233
doi: 10.1038/s41598-024-77352-3
pii: 10.1038/s41598-024-77352-3
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
25755Subventions
Organisme : National Natural Science Foundation of China
ID : 21864008
Organisme : National Natural Science Foundation of China
ID : 82360700
Organisme : Guizhou Provincial Science and Technology Department
ID : [2018]1130
Organisme : Guizhou Provincial Science and Technology Department
ID : ZK[2021]045
Organisme : Excellent Young Talents Plan of Guizhou Medical University
ID : [2021]104
Informations de copyright
© 2024. The Author(s).
Références
Roth, G. A. et al. Global burden of cardiovascular diseases and risk factors, 1990–2019: Update from the GBD 2019 study. J. Am. Coll. Cardiol. 76, 2982–3021 (2020).
pubmed: 33309175
pmcid: 7755038
doi: 10.1016/j.jacc.2020.11.010
Feigin, V. L. et al. Global, regional, and national burden of stroke and its risk factors, 1990–2019: A systematic analysis for the global burden of disease study 2019. Lancet Neurol. 20, 795–820 (2021).
doi: 10.1016/S1474-4422(21)00252-0
Tsao, C. W. et al. Heart disease and stroke statistics—2023 update: A report from the American heart association. Circulation. 147, e93–e621 (2023).
pubmed: 36695182
doi: 10.1161/CIR.0000000000001123
Jensen, R. V., Hjortbak, M. V. & Bøtker, H. E. Ischemic heart disease: An update. Semin Nucl. Med. 50, 195–207 (2020).
pubmed: 32284106
doi: 10.1053/j.semnuclmed.2020.02.007
Di Biase, L., Bonura, A., Caminiti, M. L., Pecoraro, P. M. & Di Lazzaro, V. Neurophysiology tools to lower the stroke onset to treatment time during the golden hour: Microwaves, bioelectrical impedance and near infrared spectroscopy. Ann. Med. 54, 2658–2671 (2022).
pubmed: 36154386
pmcid: 9542520
doi: 10.1080/07853890.2022.2124448
Xu, M., Liu, P. P. & Li, H. Innate immune signaling and its role in metabolic and cardiovascular diseases. Physiol. Rev. 99, 893–948 (2019).
pubmed: 30565509
doi: 10.1152/physrev.00065.2017
Cai, J. J., Xu, M., Zhang, X. J. & Li, H. L. Innate immune signaling in nonalcoholic fatty liver disease and cardiovascular diseases. Annu. Rev. Pathol. 14, 153–184 (2019).
pubmed: 30230967
doi: 10.1146/annurev-pathmechdis-012418-013003
Piccirillo, F. et al. Changes of the coronary arteries and cardiac microvasculature with aging: Implications for translational research and clinical practice. Mech. Ageing Dev. 184, 111161 (2019).
pubmed: 31647940
doi: 10.1016/j.mad.2019.111161
Severino, P. et al. Ischemic heart disease pathophysiology paradigms overview: From plaque activation to microvascular dysfunction. Int. J. Mol. Sci. 21, 8118 (2020).
pubmed: 33143256
pmcid: 7663258
doi: 10.3390/ijms21218118
Doran, S. et al. Multi-omics approaches for revealing the complexity of cardiovascular disease. Brief. Bioinform. 22, bbab061 (2021).
pubmed: 33725119
pmcid: 8425417
doi: 10.1093/bib/bbab061
Fangma, Y. J., Liu, M. T., Liao, J., Chen, Z. & Zheng, Y. R. Dissecting the brain with spatially resolved multi-omics. J. Pharm. Anal. 13, 694–710 (2023).
pubmed: 37577383
pmcid: 10422112
doi: 10.1016/j.jpha.2023.04.003
Pulit, S. L. et al. Loci associated with ischaemic stroke and its subtypes (SiGN): A genome-wide association study. Lancet Neurol. 15, 174–184 (2016).
doi: 10.1016/S1474-4422(15)00338-5
Holliday, E. G. et al. Common variants at 6p21.1 are associated with large artery atherosclerotic stroke. Nat. Genet. 44, 1147–1151 (2012).
pubmed: 22941190
doi: 10.1038/ng.2397
Traylor, M. et al. Genetic basis of lacunar stroke: A pooled analysis of individual patient data and genome-wide association studies. Lancet Neurol. 20, 351–361 (2021).
pubmed: 33773637
pmcid: 8062914
doi: 10.1016/S1474-4422(21)00031-4
Lind, L. et al. Large-scale plasma protein profiling of incident myocardial infarction, ischemic stroke, and heart failure. J. Am. Heart Assoc. 10, e023330 (2021).
pubmed: 34845919
pmcid: 9075402
doi: 10.1161/JAHA.121.023330
Chen, Y., Li, E. M. & Xu, L. Y. Guide to metabolomics analysis: A bioinformatics workflow. Metabolites. 12, 357 (2022).
pubmed: 35448542
pmcid: 9032224
doi: 10.3390/metabo12040357
Harshfield, E. L. et al. Metabolomic profiling in small vessel disease identifies multiple associations with disease severity. Brain. 145, 2461–2471 (2022).
pubmed: 35254405
pmcid: 9337813
doi: 10.1093/brain/awac041
Razavi, A. C. et al. Novel findings from a metabolomics study of left ventricular diastolic function: The Bogalusa Heart Study. J. Am. Heart Assoc. 9, e015118 (2020).
pubmed: 31992159
pmcid: 7033875
doi: 10.1161/JAHA.119.015118
Borges, M. C. et al. Circulating fatty acids and risk of coronary heart disease and stroke: Individual participant data meta-analysis in up to 16 126 participants. J. Am. Heart Assoc. 9, e013131 (2020).
pubmed: 32114887
pmcid: 7335585
doi: 10.1161/JAHA.119.013131
Pezzatti, J. et al. Implementation of liquid chromatography-high resolution mass spectrometry methods for untargeted metabolomic analyses of biological samples: A tutorial. Anal. Chim. Acta. 1105, 28–44 (2020).
pubmed: 32138924
doi: 10.1016/j.aca.2019.12.062
Tabrez, S., Shait Mohammed, M. R., Jabir, N. R. & Khan, M. I. Identification of novel cardiovascular disease associated metabolites using untargeted metabolomics. Biol. Chem. 402, 749–757 (2021).
pubmed: 33951765
doi: 10.1515/hsz-2020-0331
Zhang, J. et al. Identification of biomarkers for risk assessment of arsenicosis based on untargeted metabolomics and machine learning algorithms. Sci. Total Environ. 870, 161861 (2023).
pubmed: 36716877
doi: 10.1016/j.scitotenv.2023.161861
Dührkop, K. et al. SIRIUS 4: A rapid tool for turning tandem mass spectra into metabolite structure information. Nat. Methods. 16, 299–302 (2019).
pubmed: 30886413
doi: 10.1038/s41592-019-0344-8
Sumner, L. W. A. et al. Proposed minimum reporting standards for chemical analysis chemical analysis working group (CAWG) metabolomics standards initiative (MSI). Metabolomics. 3, 211–221 (2007).
pubmed: 24039616
pmcid: 3772505
doi: 10.1007/s11306-007-0082-2
Kanehisa, M. et al. KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353–D361 (2017).
pubmed: 27899662
doi: 10.1093/nar/gkw1092
Wishart, D. S. et al. HMDB 5.0: The human metabolome database for 2022. Nucleic Acids Res. 50, D622–D631 (2022).
pubmed: 34986597
doi: 10.1093/nar/gkab1062
Hastings, J. et al. ChEBI in 2016: Improved services and an expanding collection of metabolites. Nucleic Acids Res. 44, D1214–D1219 (2016).
pubmed: 26467479
doi: 10.1093/nar/gkv1031
Kim, S. et al. PubChem 2019 update: Improved access to chemical data. Nucleic Acids Res. 47, D1102–D1109 (2019).
pubmed: 30371825
doi: 10.1093/nar/gky1033
Pang, Z. Q. et al. Using MetaboAnalyst 5.0 for LC-HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data. Nat. Protoc. 17, 1735–1761 (2022).
pubmed: 35715522
doi: 10.1038/s41596-022-00710-w
López-Ibáñez, J., Pazos, F. & Chagoyen, M. MBROLE 2.0-functional enrichment of chemical compounds. Nucleic Acids Res. 44, W201–W204 (2016).
pubmed: 27084944
pmcid: 4987872
doi: 10.1093/nar/gkw253
Zhang, J., Cui, X. Y., Cai, W. S. & Shao, X. G. A variable importance criterion for variable selection in near-infrared spectral analysis. Sci. China Chem. 62, 271–279 (2019).
doi: 10.1007/s11426-018-9368-9
Ward, A. et al. Machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population. NPJ Digit. Med. 3, 125 (2020).
pubmed: 33043149
pmcid: 7511400
doi: 10.1038/s41746-020-00331-1
Tsugawa, H. et al. Hydrogen rearrangement rules: Computational MS/MS fragmentation and structure elucidation using MS-FINDER software. Anal. Chem. 88, 7946–7958 (2016).
pubmed: 27419259
pmcid: 7063832
doi: 10.1021/acs.analchem.6b00770
mzCloud. https://www.mzcloud.org (accessed on December 8 2022).
Ruttkies, C., Schymanski, E. L., Wolf, S., Hollender, J. & Neumann, S. MetFrag relaunched: Incorporating strategies beyond in silico fragmentation. J. Cheminform. 8, 1–16 (2016).
doi: 10.1186/s13321-016-0115-9
Kumar, L. & Futschik, M. E. Mfuzz: A software package for soft clustering of microarray data. Bioinformation. 2, 5–7 (2007).
pubmed: 18084642
pmcid: 2139991
doi: 10.6026/97320630002005
Mannheim, D. et al. Enhanced expression of Lp-PLA2 and lysophosphatidylcholine in symptomatic carotid atherosclerotic plaques. Stroke. 39, 1448–1455 (2008).
pubmed: 18356547
pmcid: 4360896
doi: 10.1161/STROKEAHA.107.503193
Belayev, L., Khoutorova, L., Atkins, K. D. & Bazan, N. G. Robust docosahexaenoic acid-mediated neuroprotection in a rat model of transient, focal cerebral ischemia. Stroke. 40, 3121–3126 (2009).
pubmed: 19542051
pmcid: 2745047
doi: 10.1161/STROKEAHA.109.555979
Wang, L. et al. Triglyceride-rich lipoprotein lipolysis releases neutral and oxidized FFAs that induce endothelial cell inflammation. J. Lipid Res. 50, 204–213 (2009).
pubmed: 18812596
pmcid: 2636918
doi: 10.1194/jlr.M700505-JLR200
Toborek, M. et al. Linoleic acid and TNF-alpha cross-amplify oxidative injury and dysfunction of endothelial cells. J. Lipid Res. 37, 123–135 (1996).
pubmed: 8820108
doi: 10.1016/S0022-2275(20)37641-0
Ebert, D., Haller, R. G. & Walton, M. E. Energy contribution of octanoate to intact rat brain metabolism measured by 13 C nuclear magnetic resonance spectroscopy. J. Neurosci. 23, 5928–5935 (2003).
pubmed: 12843297
pmcid: 6741266
doi: 10.1523/JNEUROSCI.23-13-05928.2003
Murphy, T. H., Li, P., Betts, K. & Liu, R. Two-photon imaging of stroke onset in vivo reveals that NMDA-receptor independent ischemic depolarization is the major cause of rapid reversible damage to dendrites and spines. J. Neurosci. 28, 1756–1772 (2008).
pubmed: 18272696
pmcid: 6671530
doi: 10.1523/JNEUROSCI.5128-07.2008
Wang, X. et al. Changes of metabolites in acute ischemic stroke and its subtypes. Front. Neurosci. 14, 580929 (2021).
pubmed: 33505234
pmcid: 7829509
doi: 10.3389/fnins.2020.580929
Schwartz, M. W., Woods, S. C., Porte, D. Jr, Seeley, R. J. & Baskin, D. G. Central nervous system control of food intake. Nature. 404, 661–671 (2000).
pubmed: 10766253
doi: 10.1038/35007534
Rother, E. et al. Subtype-selective antagonists of lysophosphatidic acid receptors inhibit platelet activation triggered by the lipid core of atherosclerotic plaques. Circulation. 108, 741–747 (2003).
pubmed: 12885756
doi: 10.1161/01.CIR.0000083715.37658.C4
Guasch-Ferré, M. et al. Plasma metabolites from choline pathway and risk of cardiovascular disease in the PREDIMED (prevention with mediterranean diet) study. J. Am. Heart Assoc. 6, e006524 (2017).
pubmed: 29080862
pmcid: 5721752
doi: 10.1161/JAHA.117.006524
Haghikia, A. et al. Gut microbiota–dependent trimethylamine N-oxide predicts risk of cardiovascular events in patients with stroke and is related to proinflammatory monocytes. Arterioscl Throm Vas. 38, 2225–2235 (2018).
doi: 10.1161/ATVBAHA.118.311023