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. 2024 May 13;13(10):2866.
doi: 10.3390/jcm13102866.

Gastrointestinal Myoelectrical Activity (GIMA) Biomarker for Noninvasive Diagnosis of Endometriosis

Mark Noar  1 , John Mathias  2   3 , Ajit Kolatkar  4
Affiliations

Gastrointestinal Myoelectrical Activity (GIMA) Biomarker for Noninvasive Diagnosis of Endometriosis

Mark Noar et al. J Clin Med. .

Abstract

Background/Objectives: Endometriosis represents substantial direct and indirect healthcare costs impacted by an absence of uniformly accurate, non-invasive diagnostic tools. We endeavored to demonstrate gastrointestinal myoelectrical activity (GIMA) biomarkers, unique to endometriosis, will allow non-invasive, uniformly accurate diagnosis or exclusion of endometriosis. Methods: Prospective open-label comparative study of 154 patients, age ≥ 18, with or without diagnosed endometriosis. Population included 62 non-endometriosis controls (Cohort 1), 43 subjects with surgically/histologically confirmed endometriosis (Cohort 2), and 49 subjects with abdominal pain and negative imaging (Cohort 3). Non-invasive electroviscerography (EVG) recorded GIMA biomarkers from three abdominal electrodes before and 30 min post water load protocol. Cohort 2 had postoperative EVG and Cohort 3 had preoperative EVG. Calculated specificity, sensitivity, negative predictive value (NPV), positive predictive value (PPV), and predictive probability or C-statistic used univariate, multivariate, linear, and logistical regression analyses of the area under the curve (AUC) at all frequency and time points, including age and pain covariants. Results: The non-endometriosis cohort differed significantly from the endometriosis cohorts (p < 0.001) for median (IQR) and AUC percent frequency distribution of power at baseline, 10 min, 20 min, and 30 min post water load at all frequency ranges: 15-20 cpm, 30-40 cpm, and 40-50 cpm. The endometriosis cohorts were statistically similar (p > 0.05). GIMA biomarker threshold scoring demonstrated 95%/91% sensitivity and PPV, 96%/95% specificity and NPV, and a C-statistic of >99%/98%, respectively, for age subsets. GIMA biomarkers in Cohort 3 predicted 47/49 subjects positive and 2/49 negative for endometriosis, confirmed surgically. Hormonal therapy, surgical stage, nor pain score affected diagnostic accuracy. Conclusions: EVG with GIMA biomarker detection distinguished participants with and without endometriosis based upon endometriosis-specific GIMA biomarkers threshold scoring.

Keywords: GIMA biomarker threshold score; biomarker; electroviscerogram; electroviscerography; endometriosis; gastrointestinal myoelectrical activity (GIMA); non-invasive electroviscerography; predictive modeling; water load satiety test.

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Conflict of interest statement

M.N. reports a relationship with Endosure, Inc as a founder and board member. M.N. has patent #7,160,254 licensed to Endosure, Inc. The other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1
Figure 1
EVG system with (A): tricorder-3l, (B): respiratory belt, and (C): dry gel electrodes.
Figure 2
Figure 2
Cohort stratification.
Figure 3
Figure 3
Running spectral analysis of Cohort 1. Power of frequency distribution of GIMA characteristics at frequencies (10–60 cpm) among non-endometriosis participants, who were with or without symptoms at baseline and 10 min, 20 min, and 30 min post water load.
Figure 4
Figure 4
Running spectral analysis of (A) Cohort 2 and (B) Cohort 3. Power of frequency distribution of GIMA characteristics at frequencies (10–60 cpm) among subjects with endometriosis, Cohort 2 and Cohort 3, at Baseline and 10 min, 20 min and 30 min post water load.
Figure 4
Figure 4
Running spectral analysis of (A) Cohort 2 and (B) Cohort 3. Power of frequency distribution of GIMA characteristics at frequencies (10–60 cpm) among subjects with endometriosis, Cohort 2 and Cohort 3, at Baseline and 10 min, 20 min and 30 min post water load.
Figure 5
Figure 5
Box plots, panels A-F, depict comparison of distribution of GIMA characteristics using median (IQR). Unadjusted median and interquartile ranges for frequencies (10–60 cpm) among healthy controls (Cohort 1) versus subjects with endometriosis (Cohorts 2 and 3) at BL, 10 min, 20 min and 30 min. (A) = EVG Frequencies 10–15 cpm, (B) = EVG Frequencies 15–20 cpm, (C) = EVG Frequencies 20–30 cpm, (D) = EVG Frequencies 30–40 cpm, (E) = EVG Frequencies 40–50 cpm, and (F) = EVG Frequencies 50–60 cpm).
Figure 6
Figure 6
Distribution of AUC for frequencies between healthy, non-endometriosis controls (Cohort 1) and subjects with endometriosis (Cohorts 2 and 3). Box plots were used to compare distribution of AUC between controls and cases for a given frequency. For all frequencies, p value was less than 0.001 while comparing Cohort 1 and Cohort 2 and Cohort 1 and Cohort 3 from 15 cpm to 60 cpm.
Figure 7
Figure 7
(A). ROC curve for disease predictability age ≤ 35 years. Area under the ROC curve of GIMA biomarker AUC is 0.9979. (B). ROC curve for disease predictability age ≥ 36 years. Area under the ROC curve of GIMA biomarker AUC is 0.9847. The closer to a value of 1.0, the higher the predictive value of the test.
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