Research

15 pages, 1407 KiB

Open AccessArticle

Two-Stage Deep-Learning Classifier for Diagnostics of Lung Cancer Using Metabolites

by Ashvin Choudhary, Jianpeng Yu, Valentina L. Kouznetsova, Santosh Kesari and Igor F. Tsigelny

Metabolites 2023, 13(10), 1055; https://doi.org/10.3390/metabo13101055 - 07 Oct 2023

Viewed by 1122

We developed a machine-learning system for the selective diagnostics of adenocarcinoma (AD), squamous cell carcinoma (SQ), and small-cell carcinoma lung (SC) cancers based on their metabolomic profiles. The system is organized as two-stage binary classifiers. The best accuracy for classification is 92%. We [...] Read more.

We developed a machine-learning system for the selective diagnostics of adenocarcinoma (AD), squamous cell carcinoma (SQ), and small-cell carcinoma lung (SC) cancers based on their metabolomic profiles. The system is organized as two-stage binary classifiers. The best accuracy for classification is 92%. We used the biomarkers sets that contain mostly metabolites related to cancer development. Compared to traditional methods, which exclude hierarchical classification, our method splits a challenging multiclass task into smaller tasks. This allows a two-stage classifier, which is more accurate in the scenario of lung cancer classification. Compared to traditional methods, such a “divide and conquer strategy” gives much more accurate and explainable results. Such methods, including our algorithm, allow for the systematic tracking of each computational step. Full article

(This article belongs to the Special Issue Personalized Metabolomics)

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18 pages, 4214 KiB

Open AccessArticle

Clinical Blood Metabogram: Application to Overweight and Obese Patients

by Petr G. Lokhov, Elena E. Balashova, Oxana P. Trifonova, Dmitry L. Maslov, Oksana A. Plotnikova, Khaider K. Sharafetdinov, Dmitry B. Nikityuk, Victor A. Tutelyan, Elena A. Ponomarenko and Alexander I. Archakov

Metabolites 2023, 13(7), 798; https://doi.org/10.3390/metabo13070798 - 27 Jun 2023

Cited by 2 | Viewed by 980

Abstract

Recently, the concept of a mass spectrometric blood metabogram was introduced, which allows the analysis of the blood metabolome in terms of the time, cost, and reproducibility of clinical laboratory tests. It was demonstrated that the components of the metabogram are related groups [...] Read more.

Recently, the concept of a mass spectrometric blood metabogram was introduced, which allows the analysis of the blood metabolome in terms of the time, cost, and reproducibility of clinical laboratory tests. It was demonstrated that the components of the metabogram are related groups of the blood metabolites associated with humoral regulation; the metabolism of lipids, carbohydrates, and amines; lipid intake into the organism; and liver function, thereby providing clinically relevant information. The purpose of this work was to evaluate the relevance of using the metabogram in a disease. To do this, the metabogram was used to analyze patients with various degrees of metabolic alterations associated with obesity. The study involved 20 healthy individuals, 20 overweight individuals, and 60 individuals with class 1, 2, or 3 obesity. The results showed that the metabogram revealed obesity-associated metabolic alterations, including changes in the blood levels of steroids, amino acids, fatty acids, and phospholipids, which are consistent with the available scientific data to date. Therefore, the metabogram allows testing of metabolically unhealthy overweight or obese patients, providing both a general overview of their metabolic alterations and detailing their individual characteristics. It was concluded that the metabogram is an accurate and clinically applicable test for assessing an individual’s metabolic status in disease. Full article

(This article belongs to the Special Issue Personalized Metabolomics)

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18 pages, 2436 KiB

Open AccessArticle

Life-Threatening Cardiogenic Shock Related to Venlafaxine Poisoning—A Case Report with Metabolomic Approach

by Romain Magny, Bruno Mégarbane, Pauline Guillaud, Lucie Chevillard, Nicolas Auzeil, Pauline Thiebot, Sebastian Voicu, Isabelle Malissin, Nicolas Deye, Laurence Labat and Pascal Houzé

Metabolites 2023, 13(3), 353; https://doi.org/10.3390/metabo13030353 - 27 Feb 2023

Cited by 8 | Viewed by 1809

Abstract

Metabolomics in clinical toxicology aim at reliably identifying and semi-quantifying a broad array of endogenous and exogenous metabolites using dedicated analytical methods. Here, we developed a three-step-based workflow to investigate the metabolic impact of the antidepressant drug venlafaxine in a poisoned patient who [...] Read more.

Metabolomics in clinical toxicology aim at reliably identifying and semi-quantifying a broad array of endogenous and exogenous metabolites using dedicated analytical methods. Here, we developed a three-step-based workflow to investigate the metabolic impact of the antidepressant drug venlafaxine in a poisoned patient who developed life-threatening cardiac failure managed with extracorporeal membrane oxygenation. Both targeted quantitative and untargeted semi-quantitative metabolomic analyses using liquid chromatography hyphenated to high-resolution tandem mass spectrometry were performed to determine the plasma kinetics of venlafaxine, O-desmethyl-venlafaxine, and N-desmethyl-venlafaxine and to identify sixteen different venlafaxine-derived metabolites including one unknown (i.e., venlafaxine conjugated to a hexosyl-radical), respectively. Correlations between the quantitative metabolomic data and annotated endogenous metabolites suggested impaired amino acid and lipid metabolism, Krebs cycle, and kynurenine pathway. This preliminary study represents a first step towards a more extensive application of toxicometabolomics in clinical toxicology and a useful workflow to identify the biomarkers of toxicity. Full article

(This article belongs to the Special Issue Personalized Metabolomics)

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16 pages, 1616 KiB

Open AccessArticle

Identification of Exhaled Metabolites in Children with Cystic Fibrosis

by Ronja Weber, Nathan Perkins, Tobias Bruderer, Srdjan Micic and Alexander Moeller

Metabolites 2022, 12(10), 980; https://doi.org/10.3390/metabo12100980 - 17 Oct 2022

Cited by 4 | Viewed by 1632

Abstract

The early detection of inflammation and infection is important to prevent irreversible lung damage in cystic fibrosis. Novel and non-invasive monitoring tools would be of high benefit for the quality of life of patients. Our group previously detected over 100 exhaled mass-to-charge ( [...] Read more.

The early detection of inflammation and infection is important to prevent irreversible lung damage in cystic fibrosis. Novel and non-invasive monitoring tools would be of high benefit for the quality of life of patients. Our group previously detected over 100 exhaled mass-to-charge (m/z) features, using on-line secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS), which distinguish children with cystic fibrosis from healthy controls. The aim of this study was to annotate as many m/z features as possible with putative chemical structures. Compound identification was performed by applying a rigorous workflow, which included the analysis of on-line MS² spectra and a literature comparison. A total of 49 discriminatory exhaled compounds were putatively identified. A group of compounds including glycolic acid, glyceric acid and xanthine were elevated in the cystic fibrosis group. A large group of acylcarnitines and aldehydes were found to be decreased in cystic fibrosis. The proposed compound identification workflow was used to identify signatures of volatile organic compounds that discriminate children with cystic fibrosis from healthy controls, which is the first step for future non-invasive and personalized applications. Full article

(This article belongs to the Special Issue Personalized Metabolomics)

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12 pages, 1342 KiB

Open AccessArticle

Non-Invasive Differential Diagnosis of Cervical Neoplastic Lesions by the Lipid Profile Analysis of Cervical Scrapings

by Alisa Tokareva, Vitaliy Chagovets, Djamilja Attoeva, Natalia Starodubtseva, Niso Nazarova, Kirill Gusakov, Eugenii Kukaev, Vladimir Frankevich and Gennady Sukhikh

Metabolites 2022, 12(9), 883; https://doi.org/10.3390/metabo12090883 - 19 Sep 2022

Cited by 1 | Viewed by 1562

Abstract

Cervical cancer is one of the most common cancers in women with pronounced stages of precancerous lesions. Accurate differential diagnosis of such lesions is one of the primary challenges of medical specialists, which is vital to improving patient survival. The aim of this [...] Read more.

Cervical cancer is one of the most common cancers in women with pronounced stages of precancerous lesions. Accurate differential diagnosis of such lesions is one of the primary challenges of medical specialists, which is vital to improving patient survival. The aim of this study was to develop and test an algorithm for the differential diagnosis of cervical lesions based on lipid levels in scrapings from the cervical epithelium and cervicovaginal canal. The lipid composition of the samples was analyzed by high-performance chromato-mass spectrometry. Lipid markers were selected using the Mann–Whitney test with a cutoff value of 0.05 and by projections to latent structures discriminant analysis, where a projection threshold of one was chosen. The final selection of variables for binomial logistic regressions was carried out using the Akaike information criterion. As a result, a final neoplasia classification method, based on 20 logistic regression sub-models, has an accuracy of 79% for discrimination NILM/cervicitis/LSIL/HSIL/cancer. The model has a sensitivity of 83% and a specificity of 88% for discrimination of several lesions (HSIL and cancer). This allows us to discuss the prospective viability of further validation of the developed non-invasive method of differential diagnosis. Full article

(This article belongs to the Special Issue Personalized Metabolomics)

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Review

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15 pages, 1605 KiB

Open AccessReview

Current State and Future Perspectives on Personalized Metabolomics

by Oxana P. Trifonova, Dmitry L. Maslov, Elena E. Balashova and Petr G. Lokhov

Metabolites 2023, 13(1), 67; https://doi.org/10.3390/metabo13010067 - 01 Jan 2023

Cited by 3 | Viewed by 2284

Abstract

Metabolomics is one of the most promising ‘omics’ sciences for the implementation in medicine by developing new diagnostic tests and optimizing drug therapy. Since in metabolomics, the end products of the biochemical processes in an organism are studied, which are under the influence [...] Read more.

Metabolomics is one of the most promising ‘omics’ sciences for the implementation in medicine by developing new diagnostic tests and optimizing drug therapy. Since in metabolomics, the end products of the biochemical processes in an organism are studied, which are under the influence of both genetic and environmental factors, the metabolomics analysis can detect any changes associated with both lifestyle and pathological processes. Almost every case-controlled metabolomics study shows a high diagnostic accuracy. Taking into account that metabolomics processes are already described for most nosologies, there are prerequisites that a high-speed and comprehensive metabolite analysis will replace, in near future, the narrow range of chemical analyses used today, by the medical community. However, despite the promising perspectives of personalized metabolomics, there are currently no FDA-approved metabolomics tests. The well-known problem of complexity of personalized metabolomics data analysis and their interpretation for the end-users, in addition to a traditional need for analytical methods to address the quality control, standardization, and data treatment are reported in the review. Possible ways to solve the problems and change the situation with the introduction of metabolomics tests into clinical practice, are also discussed. Full article

(This article belongs to the Special Issue Personalized Metabolomics)

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36 pages, 1882 KiB

Open AccessReview

Blood and Urinary Biomarkers of Antipsychotic-Induced Metabolic Syndrome

by Aiperi K. Khasanova, Vera S. Dobrodeeva, Natalia A. Shnayder, Marina M. Petrova, Elena A. Pronina, Elena N. Bochanova, Natalia V. Lareva, Natalia P. Garganeeva, Daria A. Smirnova and Regina F. Nasyrova

Metabolites 2022, 12(8), 726; https://doi.org/10.3390/metabo12080726 - 05 Aug 2022

Cited by 6 | Viewed by 2975

Abstract

Metabolic syndrome (MetS) is a clustering of at least three of the following five medical conditions: abdominal obesity, high blood pressure, high blood sugar, high serum triglycerides, and low serum high-density lipoprotein (HDL). Antipsychotic (AP)-induced MetS (AIMetS) is the most common adverse drug [...] Read more.

Metabolic syndrome (MetS) is a clustering of at least three of the following five medical conditions: abdominal obesity, high blood pressure, high blood sugar, high serum triglycerides, and low serum high-density lipoprotein (HDL). Antipsychotic (AP)-induced MetS (AIMetS) is the most common adverse drug reaction (ADR) of psychiatric pharmacotherapy. Herein, we review the results of studies of blood (serum and plasma) and urinary biomarkers as predictors of AIMetS in patients with schizophrenia (Sch). We reviewed 1440 studies examining 38 blood and 19 urinary metabolic biomarkers, including urinary indicators involved in the development of AIMetS. Among the results, only positive associations were revealed. However, at present, it should be recognized that there is no consensus on the role of any particular urinary biomarker of AIMetS. Evaluation of urinary biomarkers of the development of MetS and AIMetS, as one of the most common concomitant pathological conditions in the treatment of patients with psychiatric disorders, may provide a key to the development of strategies for personalized prevention and treatment of the condition, which is considered a complication of AP therapy for Sch in clinical practice. Full article

(This article belongs to the Special Issue Personalized Metabolomics)

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Other

Jump to: Research, Review

19 pages, 6646 KiB

Open AccessSystematic Review

The Human Skin Volatolome: A Systematic Review of Untargeted Mass Spectrometry Analysis

by Anuja Mitra, Sunyoung Choi, Piers R. Boshier, Alexandra Razumovskaya-Hough, Ilaria Belluomo, Patrik Spanel and George B. Hanna

Metabolites 2022, 12(9), 824; https://doi.org/10.3390/metabo12090824 - 01 Sep 2022

Cited by 6 | Viewed by 2881

Abstract

The analysis of volatile organic compounds (VOCs) can provide important clinical information (entirely non-invasively); however, the exact extent to which VOCs from human skin can be signatures of health and disease is unknown. This systematic review summarises the published literature concerning the methodology, [...] Read more.

The analysis of volatile organic compounds (VOCs) can provide important clinical information (entirely non-invasively); however, the exact extent to which VOCs from human skin can be signatures of health and disease is unknown. This systematic review summarises the published literature concerning the methodology, application, and volatile profiles of skin VOC studies. An online literature search was conducted in accordance with the preferred reporting items for systematic reviews and meta-analysis, to identify human skin VOC studies using untargeted mass spectrometry (MS) methods. The principal outcome was chemically verified VOCs detected from the skin. Each VOC was cross-referenced using the CAS number against the Human Metabolome and KEGG databases to evaluate biological origins. A total of 29 studies identified 822 skin VOCs from 935 participants. Skin VOCs were commonly sampled from the hand (n = 9) or forearm (n = 7) using an absorbent patch (n = 15) with analysis by gas chromatography MS (n = 23). Twenty-two studies profiled the skin VOCs of healthy subjects, demonstrating a volatolome consisting of aldehydes (18%), carboxylic acids (12%), alkanes (12%), fatty alcohols (9%), ketones (7%), benzenes and derivatives (6%), alkenes (2%), and menthane monoterpenoids (2%). Of the VOCs identified, 13% had putative endogenous origins, 46% had tentative exogenous origins, and 40% were metabolites from mixed metabolic pathways. This review has comprehensively profiled the human skin volatolome, demonstrating the presence of a distinct VOC signature of healthy skin, which can be used as a reference for future researchers seeking to unlock the clinical potential of skin volatolomics. As significant proportions of identified VOCs have putative exogenous origins, strategies to minimise their presence through methodological refinements and identifying confounding compounds are discussed. Full article

(This article belongs to the Special Issue Personalized Metabolomics)

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