Predicting the Rapid Progression of Mild Cognitive Impairment by Intestinal Flora and Blood Indicators through Machine Learning Method.

IF 1.9 4区医学 Q3 CLINICAL NEUROLOGY

Neurodegenerative Diseases Pub Date : 2023-01-01 Epub Date: 2024-02-28 DOI:10.1159/000538023

Lingling Wang, Jing Yan, Huiqin Liu, Xiaohui Zhao, Haihan Song, Juan Yang

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引用次数: 0

Abstract

Introduction: The aim of the work was to establish a prediction model of mild cognitive impairment (MCI) progression based on intestinal flora by machine learning method.

Method: A total of 1,013 patients were recruited, in which 87 patients with MCI finished a two-year follow-up. To establish a prediction model, 61 patients were randomly divided into a training set and 26 patients were divided into a testing set. A total of 121 features including demographic characteristics, hematological indicators, and intestinal flora abundance were analyzed.

Results: Of the 87 patients who finished a two-year follow-up, 44 presented rapid progression. Model 1 was established based on 121 features with the accuracy 85%, sensitivity 85%, and specificity 83%. Model 2 was based on the first fifteen features of model 1 (triglyceride, uric acid, alanine transaminase, F-Clostridiaceae, G-Megamonas, S-Megamonas, G-Shigella, G-Shigella, S-Shigella, average hemoglobin concentration, G-Alistipes, S-Collinsella, median cell count, average hemoglobin volume, low-density lipoprotein), with the accuracy 97%, sensitivity 92%, and specificity 100%. Model 3 was based on the first ten features of model 1, with the accuracy 97%, sensitivity 86%, and specificity 100%. Other models based on the demographic characteristics, hematological indicators, or intestinal flora abundance features presented lower sensitivity and specificity.

Conclusion: The 15 features (including intestinal flora abundance) could establish an effective model for predicting rapid MCI progression.

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通过机器学习方法，利用肠道菌群和血液指标预测轻度认知障碍的快速进展。

引言这项研究的目的是通过机器学习方法建立一个基于肠道菌群的轻度认知障碍（MCI）进展预测模型：方法：共招募了 1013 名患者，其中 87 名 MCI 患者完成了为期两年的随访。为了建立预测模型，61 名患者被随机分为训练集，26 名患者被分为测试集。结果：结果：在完成两年随访的 87 名患者中，44 人的病情发展迅速。模型 1 基于 121 个特征建立，准确率为 85%，灵敏度为 85%，特异性为 83%。模型 2 基于模型 1 的前 15 个特征（甘油三酯、尿酸、丙氨酸转氨酶、F-梭状芽孢杆菌、G-麦加莫纳菌、S-麦加莫纳菌、G-志贺菌、G-志贺菌、S-志贺菌、平均血红蛋白浓度、G-Alistipes、S-Collinsella、中位细胞计数、平均血红蛋白体积、低密度脂蛋白），准确率为 97%，灵敏度为 92%，特异性为 100%。模型 3 基于模型 1 的前十个特征，准确率为 97%，灵敏度为 86%，特异性为 100%。其他基于人口统计学特征、血液学指标或肠道菌群丰富度特征的模型的灵敏度和特异性较低：结论：15 个特征（包括肠道菌群丰富度）可以建立一个预测 MCI 快速进展的有效模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Neurodegenerative Diseases 医学-临床神经学

CiteScore

5.90

自引率

0.00%

发文量

审稿时长

6-12 weeks

期刊介绍： ''Neurodegenerative Diseases'' is a bimonthly, multidisciplinary journal for the publication of advances in the understanding of neurodegenerative diseases, including Alzheimer''s disease, Parkinson''s disease, amyotrophic lateral sclerosis, Huntington''s disease and related neurological and psychiatric disorders.