Evidence map›Paper›PMID 36557222›Full record

ArticleMetabolites2022

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

Natalia E Moskaleva, Ksenia M Shestakova, Alexey V Kukharenko, Pavel A Markin, Maria V Kozhevnikova, Ekaterina O Korobkova, Alex Brito, Sabina N Baskhanova, Natalia V Mesonzhnik, Yuri N Belenkov and 4 more

Open access · goldAbstract read
In one paragraph

Article in Metabolites, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.6field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 22 citations in OpenAlex.

  1. Article
  2. Article
  3. Artificial intelligence to investigate metabolomics data for precision medicine.Metabolomics : Official journal of the Metabolomic Society · 2026
    Review
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  14. Causal Associations Between Human Blood Metabolites and Cardio-Cerebrovascular Diseases: A Mendelian Randomisation Study.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors at 2 institutions in 2 countries.

Natalia E MoskalevaWorld-Class Research Center Digital Biodesign and Personalized Healthcare, I.M. Sechenov First Moscow State Medical University, 119435 Moscow, Russia.ORCID 0000-0002-7309-8913
Ksenia M ShestakovaWorld-Class Research Center Digital Biodesign and Personalized Healthcare, I.M. Sechenov First Moscow State Medical University, 119435 Moscow, Russia.
Alexey V KukharenkoLaboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow Medical University, 119435 Moscow, Russia.
Pavel A MarkinWorld-Class Research Center Digital Biodesign and Personalized Healthcare, I.M. Sechenov First Moscow State Medical University, 119435 Moscow, Russia.
Maria V KozhevnikovaHospital Therapy N°1 Department of the N.V. Sklifosovsky Institute of Clinical Medicine, I.M. Sechenov First Moscow Medical University, 119992 Moscow, Russia.ORCID 0000-0003-4778-7755
Ekaterina O KorobkovaHospital Therapy N°1 Department of the N.V. Sklifosovsky Institute of Clinical Medicine, I.M. Sechenov First Moscow Medical University, 119992 Moscow, Russia.ORCID 0000-0003-2873-704X
Alex BritoLaboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow Medical University, 119435 Moscow, Russia.
Sabina N BaskhanovaWorld-Class Research Center Digital Biodesign and Personalized Healthcare, I.M. Sechenov First Moscow State Medical University, 119435 Moscow, Russia.ORCID 0000-0001-8716-1672
Natalia V MesonzhnikWorld-Class Research Center Digital Biodesign and Personalized Healthcare, I.M. Sechenov First Moscow State Medical University, 119435 Moscow, Russia.ORCID 0000-0001-8860-5853
Yuri N BelenkovHospital Therapy N°1 Department of the N.V. Sklifosovsky Institute of Clinical Medicine, I.M. Sechenov First Moscow Medical University, 119992 Moscow, Russia.
Natalia V PyatigorskayaDepartment of Industrial Pharmacy, Institute of Vocational Education I.M. Sechenov First Moscow State Medical University, 119435 Moscow, Russia.
Elena TobolkinaInstitute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, 1206 Geneva, Switzerland.
Serge RudazInstitute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, 1206 Geneva, Switzerland.ORCID 0000-0002-4180-5417
Svetlana A AppolonovaLaboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow Medical University, 119435 Moscow, Russia.ORCID 0000-0002-9032-1558
Sechenov University · RUUniversity of Geneva · CH

Funding

Ministry of Science and Higher Education of the Russian Federation 075-15-2022-304
6 · The paper itself

Abstract

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.

Indexed as

acylcarnitinesamino acidscardiovascular disorderscoronary heart diseasehypertensionmachine learningmetabolitesmethylargininestranslational medicinetryptophan catabolism

Identifiers

PMID36557222
PMCPMC9781191
OpenAlexW4310232045

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.