Development of a Gastrointestinal-Myoelectrical-Activity-Based Nomogram Model for Predicting the Risk of Mild

Baichuan Li1, Shuming Ji2, Anjiao Peng1

  • 1Department of Neurology, Joint Research Institution of Altitude Health, West China Hospital, Sichuan University, Chengdu 610044, China.

Biomolecules
|December 23, 2022
PubMed

Insights

This study developed a novel risk prediction model for mild cognitive impairment (MCI) using gastrointestinal myoelectrical activity. The model shows promise for early detection and intervention, potentially aiding in Alzheimer's disease (AD) prevention.

Area of Science:

  • Neuroscience
  • Gastroenterology
  • Medical Diagnostics

Background:

  • Mild cognitive impairment (MCI) is a precursor to Alzheimer's disease (AD), necessitating early detection.
  • Current MCI screening methods face challenges, leading to underdiagnosis.
  • Gastrointestinal function is increasingly recognized for its link to neuropsychiatric conditions.

Purpose of the Study:

  • To develop and validate a risk prediction model for MCI based on gastrointestinal myoelectrical activity.
  • To explore the utility of electrogastroenterography (EGG) in identifying individuals at risk for MCI.
  • To establish a clinically applicable tool for MCI risk assessment.

Main Methods:

  • Utilized electrogastroenterography (EGG) and Mini-Mental State Examination (MMSE) on 886 participants.
  • Employed LASSO and logistic regression for risk variable selection in a training set (70%).
  • Validated the predictive model using a validation set (30%) and decision curve analysis.

Main Results:

  • Identified eight key predictors, including four EGG parameters (rhythm disturbance, dominant frequency/power ratio, intestinal channel time difference).
  • The prediction model achieved an AUC of 0.74 (training) and 0.75 (validation), demonstrating strong predictive ability.
  • Decision curve analysis indicated clinical usability for risk thresholds between 15% and 35%.

Conclusions:

  • Gastrointestinal myoelectrical activity provides a significant basis for predicting MCI risk.
  • The developed nomogram model shows potential as an alternative to traditional assessment scales for MCI.
  • This approach offers a promising avenue for early MCI detection and intervention strategies.