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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
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.
Abstract:
Mild cognitive impairment (MCI) is the prodromal stage and an important risk factor of Alzheimer's disease (AD). Interventions at the MCI stage are significant in reducing the occurrence of AD. However, there are still many obstacles to the screening of MCI, resulting in a large number of patients going undetected. Given the strong correlation between gastrointestinal function and neuropsychiatric disorders, the aim of this study is to develop a risk prediction model for MCI based on gastrointestinal myoelectrical activity. The Mini-Mental State Examination and electrogastroenterography were applied to 886 participants in western China. All participants were randomly assigned to the training and validation sets in a ratio of 7:3. In the training set, risk variables were screened using LASSO regression and logistic regression, and risk prediction models were built based on nomogram and decision curve analysis, then validation was performed. Eight predictors were selected in the training set, including four electrogastroenterography parameters (rhythm disturbance, dominant frequency and dominant power ratio of gastric channel after meal, and time difference of intestinal channel after meal). The area under the ROC curve for the prediction model was 0.74 in the training set and 0.75 in the validation set, both of which exhibited great prediction ability. Furthermore, decision curve analysis displayed that the net benefit was more desirable when the risk thresholds ranged from 15% to 35%, indicating that the nomogram was clinically usable. The model based on gastrointestinal myoelectrical activity has great significance in predicting the risk of MCI and is expected to be an alternative to scales assessment.
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.

