Gas Chromatography-Mass Spectrometry (GC-MS) Metabolites Analysis in Endometriosis Patients: A Prospective Observational Translational Study.

biomarkers endometriosis gas chromatography–mass spectrometry metabolomics multivariate analysis

Journal

Journal of clinical medicine
ISSN: 2077-0383
Titre abrégé: J Clin Med
Pays: Switzerland
ID NLM: 101606588

Informations de publication

Date de publication:
24 Jan 2023
Historique:
received: 16 11 2022
revised: 04 01 2023
accepted: 14 01 2023
entrez: 11 2 2023
pubmed: 12 2 2023
medline: 12 2 2023
Statut: epublish

Résumé

Endometriosis affects women of reproductive age, and its pathogenesis is still unclear. Typically, it overlaps other similar medical and surgical conditions, determining a delay in early diagnosis. Metabolomics allows studying metabolic changes in different physiological or pathological states to discover new potential biomarkers. We used the gas chromatography-mass spectrometer (GC-MS) to explore metabolic alterations in endometriosis to better understand its pathophysiology and find new biomarkers. Twenty-two serum samples of patients with symptomatic endometriosis and ten without it were collected and subjected to GC-MS analysis. Multivariate and univariate statistical analyses were performed, followed by pathway analysis. Partial least squares discriminant analysis was performed to determine the differences between the two groups ( The metabolomic approach identifies metabolic alterations in women with endometriosis. These findings may improve our understanding of the pathophysiological mechanisms of disease and the discovery of new biomarkers.

Sections du résumé

BACKGROUND BACKGROUND
Endometriosis affects women of reproductive age, and its pathogenesis is still unclear. Typically, it overlaps other similar medical and surgical conditions, determining a delay in early diagnosis. Metabolomics allows studying metabolic changes in different physiological or pathological states to discover new potential biomarkers. We used the gas chromatography-mass spectrometer (GC-MS) to explore metabolic alterations in endometriosis to better understand its pathophysiology and find new biomarkers.
METHODS METHODS
Twenty-two serum samples of patients with symptomatic endometriosis and ten without it were collected and subjected to GC-MS analysis. Multivariate and univariate statistical analyses were performed, followed by pathway analysis.
RESULTS RESULTS
Partial least squares discriminant analysis was performed to determine the differences between the two groups (
CONCLUSIONS CONCLUSIONS
The metabolomic approach identifies metabolic alterations in women with endometriosis. These findings may improve our understanding of the pathophysiological mechanisms of disease and the discovery of new biomarkers.

Identifiants

pubmed: 36769570
pii: jcm12030922
doi: 10.3390/jcm12030922
pmc: PMC9918082
pii:
doi:

Types de publication

Journal Article

Langues

eng

Déclaration de conflit d'intérêts

The authors declare no conflict of interest.

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Auteurs

Stefano Angioni (S)

Division of Gynecology and Obstetrics, Department of Surgical Sciences, University of Cagliari, 09124 Cagliari, Italy.

Francesca Congiu (F)

ASSL Lanusei, ATS Sardinia, Obstetrics and Gynecology Unit, 08045 Lanusei, Italy.

Salvatore Giovanni Vitale (SG)

Division of Gynecology and Obstetrics, Department of Surgical Sciences, University of Cagliari, 09124 Cagliari, Italy.

Maurizio Nicola D'Alterio (MN)

Division of Gynecology and Obstetrics, Department of Surgical Sciences, University of Cagliari, 09124 Cagliari, Italy.

Antonio Noto (A)

Department of Surgical Sciences, Neonatal Intensive Care Unit, Puericulture Institute, and Neonatal Section, University of Cagliari, 09124 Cagliari, Italy.

Giovanni Monni (G)

Department of Prenatal and Preimplantation Genetic Diagnosis and Fetal Therapy, Ospedale Pediatrico Microcitemico A. Cao, 09124 Cagliari, Italy.

Maria Laura Santoru (ML)

Department of Biomedical Sciences, Unit of Clinic Metabolomics, University of Cagliari, 09124 Cagliari, Italy.

Vassilios Fanos (V)

Department of Surgical Sciences, Neonatal Intensive Care Unit, Puericulture Institute, and Neonatal Section, University of Cagliari, 09124 Cagliari, Italy.

Federica Murgia (F)

Department of Biomedical Sciences, Unit of Clinic Metabolomics, University of Cagliari, 09124 Cagliari, Italy.

Luigi Atzori (L)

Department of Biomedical Sciences, Unit of Clinic Metabolomics, University of Cagliari, 09124 Cagliari, Italy.

Classifications MeSH