Usefulness of Machine Learning-Based Gut Microbiome Analysis for Identifying Patients with Irritable Bowels Syndrome
Usefulness of Machine Learning-Based Gut Microbiome Analysis for Identifying Patients with Irritable Bowels Syndrome
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DOI：
10.3390/jcm9082403
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发表时间：
2020-08-01
影响因子：
3.9
通讯作者：
Miwa, Hiroto
中科院分区：
文献类型：
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作者：
Fukui, Hirokazu;Nishida, Akifumi;Miwa, Hiroto
肠易激综合征（IBS）是通过主观临床症状来诊断的。我们的目标是建立一个客观的IBS预测模型，基于肠道微生物组分析，采用机器学习。我们收集了85例符合罗马III IBS标准的成人患者的粪便样本和临床数据，以及26例健康对照。通过16 S核糖体RNA测序分析粪便肠道微生物组谱，并通过气相色谱-质谱法测定短链脂肪酸。基于机器学习后的肠道微生物组数据的IBS预测模型被验证其临床诊断的一致性。IBS组的粪便微生物组α多样性指数显著小于健康对照组。肠易激综合征组丙酸含量及丁酸与戊酸之差显著高于健康对照组（p< 0.05）。使用LASSO逻辑回归，我们提取了一组特征细菌，以区分IBS患者和健康对照。使用这些特征细菌的数据，我们建立了一个通过机器学习识别IBS患者的预测模型（灵敏度>80%;特异性>90%）。使用机器学习的肠道微生物组分析对于识别IBS患者很有用。
Irritable bowel syndrome (IBS) is diagnosed by subjective clinical symptoms. We aimed to establish an objective IBS prediction model based on gut microbiome analyses employing machine learning. We collected fecal samples and clinical data from 85 adult patients who met the Rome III criteria for IBS, as well as from 26 healthy controls. The fecal gut microbiome profiles were analyzed by 16S ribosomal RNA sequencing, and the determination of short-chain fatty acids was performed by gas chromatography-mass spectrometry. The IBS prediction model based on gut microbiome data after machine learning was validated for its consistency for clinical diagnosis. The fecal microbiome alpha-diversity indices were significantly smaller in the IBS group than in the healthy controls. The amount of propionic acid and the difference between butyric acid and valerate were significantly higher in the IBS group than in the healthy controls (p< 0.05). Using LASSO logistic regression, we extracted a featured group of bacteria to distinguish IBS patients from healthy controls. Using the data for these featured bacteria, we established a prediction model for identifying IBS patients by machine learning (sensitivity >80%; specificity >90%). Gut microbiome analysis using machine learning is useful for identifying patients with IBS.