Normative Values for Body Surface Gastric Mapping Evaluations of Gastric Motility Using Gastric Alimetry: Spectral Analysis

Abstract

INTRODUCTION:Body surface gastric mapping (BSGM) is a new noninvasive test of gastric function. BSGM offers several novel and improved biomarkers of gastric function capable of differentiating patients with overlapping symptom profiles. The aim of this study was to define normative reference intervals for BSGM spectral metrics in a population of healthy controls.METHODS:BSGM was performed in healthy controls using Gastric Alimetry (Alimetry, New Zealand) comprising a stretchable high-resolution array (8 × 8 electrodes; 196 cm2), wearable Reader, and validated symptom-logging App. The evaluation encompassed a fasting baseline (30 minutes), 482 kCal meal, and 4-hour postprandial recording. Normative reference intervals were calculated for BSGM metrics including the Principal Gastric Frequency, Gastric Alimetry Rhythm Index (a measure of the concentration of power in the gastric frequency band over time), body mass index (BMI)–adjusted amplitude (μV), and fed:fasted amplitude ratio. Data were reported as median and reference interval (5th and/or 95th percentiles).RESULTS:A total of 110 subjects (55% female, median age 32 years [interquartile range 24–50], median BMI 23.8 kg/m2 [interquartile range 21.4–26.9]) were included. The median Principal Gastric Frequency was 3.04 cycles per minute; reference interval: 2.65–3.35 cycles per minute. The median Gastric Alimetry Rhythm Index was 0.50; reference interval: ≥0.25. The median BMI-adjusted amplitude was 37.6 μV; reference interval: 20–70 μV. The median fed:fasted amplitude ratio was 1.85; reference interval ≥1.08. A higher BMI was associated with a shorter meal-response duration (P = 0.014).DISCUSSION:This study provides normative reference intervals for BSGM spectral data to inform diagnostic interpretations of abnormal gastric function.

Gastric Alimetry®impacts the management pathway of chronic gastroduodenal disorders

“…These changes were justified because GET is now established to be insensitive and non-specific for neuromuscular abnormalities, whereas Gastric Alimetry is targeted toward neuromuscular profiling. (3,5,7) It should also be noted that the test was only an aid to decision making, and clinicians also considered other patient inputs at test follow-up that likely contributed to the high rate of changes in clinical care.…”

“…In particular, the framework reduces reliance on symptom criteria and GET, which present overlapping profiles,(3) and instead proposes phenotyping patients based on specific mechanisms when possible. (5–7) The phenotypes applied here cover several known gastroduodenal pathologies, including neuromuscular disorders (including interstitial cell of Cajal pathologies), visceral hypersensitivity, DGBI, and gastric outlet resistance. While these disorders are well established, they have previously been challenging to separate and apply at the individual patient level.…”

“…Gastric neuromuscular disorder: Gastric Alimetry Rhythm Index (GA-RI) <0.25, and/or low amplitudes <22 μV. (7) Managed as per gastroparesis guidelines,(1) regardless of GET status.…”

“…High sustained BMI-adjusted amplitude : >70 μV. (7) Consider distal obstruction, and evaluate GET with consideration of pyloric therapies. (8)…”

Computational models of autonomic regulation in gastric motility: Progress, challenges, and future directions

“…The feasibility of this approach for gastric clinical applications is contingent on the availability of a high-quality, predictive, and accurate mathematical model with which to generate valid training data. At present, neuromodulation of the brain-gut axis is receiving active interest, with studies that have reported recovery of gastric functions in functional dyspepsia patients ( Zhu et al, 2021 ) as well as new metrics from non-invasive body-surface gastric mapping studies for definitive of normative values and classifications of diseases ( Gharibans et al, 2022 ; Varghese et al, 2022 ). Together, these emerging metrics can be used as objective functions for brain-gut axis models to generate targets to device closed-loop protocols for controlling gastric functions via neuromodulation.…”