Metabolomic profile as a noninvasive adjunct tool for the diagnosis of Grades III and IV endometriosis‐related infertility

Abstract

The aim of the present case‐control study was to develop a noninvasive adjuvant tool for the diagnosis of endometriosis. Serum samples from 100 patients undergoing intracytoplasmic sperm injection were split into two groups according to the cause of infertility: an endometriosis group (n = 50), consisting of samples derived from patients with Grade III and IV endometriosis, and a control group (n = 50), comprising samples derived from patients with isolated male factor infertility. The metabolomic profile of each sample was obtained, through mass spectrometry. Partial least squares discriminant analysis was able to clearly classify the endometriosis and control groups. Ten potential biomarkers were selected based on their importance for model prediction. These ions were used to build the receiver‐operating characteristic curve, which presented an area under the curve of 0.904 (95% confidence interval: 0.796–0.985). To validate the model, 30 other samples from infertile women without any evidence of endometriosis were tested. Considering these ions as possible biomarkers, the model was able to correctly classify 84% of the patients. Finally, a similar prediction potential was observed in the model validated set, when samples from the disease‐free group were tested. Serum metabolomics may be useful as a noninvasive adjunct tool for the selection of patients who must undergo laparoscopy for definitive endometriosis diagnosis.

Lipidomic Alterations and PPARα Activation Induced by Resveratrol Lead to Reduction in Lesion Size in Endometriosis Models

“…Significant alterations in SM, PC, TG, and PE between the eutopic and ectopic endometriums were also noted in patients with EMs [ 16 , 17 ]. As a direct infiltration environment for ectopic endometrium, peritoneal fluid in patients with EMs was found to have a decreased PC level [ 18 , 19 ]. So far, such lipidomic analysis data has been predominantly targeted simply to find biomarkers for the clinical detection of this disease.…”

Clinical use of artificial intelligence in endometriosis: a scoping review

“…Ten studies 26,[36][37][38][39][40][41][42][43][44] used various metabolite spectra as their primary inputs to develop diagnostic and predictive models in endometriosis populations. In this specific problem formulation, it is important to note the methodology that is used.…”

“…However, although these three studies used the same type of AI intervention, the inputs varied greatly between them. Two studies used PLSDA to compute their final models 36,42 , albeit using different methodologies (mass spectroscopy 36 and 1H-NMR spectrophotometer 42 ). While the inputs also varied between both models, they both had a similar correct classification rates of 84% 36 and 86.67% 42 .…”

Metabolomics in Central Sensitivity Syndromes

“…These metabolite variances allowed endometriosis patients to be discriminated from controls with a sensitivity of 81.8% and a specificity of 91.3%. In a recent case-control study, the serum metabolomic profile was developed in women with stage III-IV endometriosis (n = 50) and controls (n = 50) using MS to test the diagnostic capabilities of serum metabolomics [80]. Using this profile, 10 metabolites were identified based on their significance to the predictive model.…”