Hu Xiefei, Zhi Shenshen, Wu Wenyan, Tao Yang, Zhang Yuanyuan, Li Lijuan, Li Xun, Pan Liyan, Fan Haiping, Li Wei
Clinical Laboratory, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China.
Chongqing Key Laboratory of Emergency Medicine, Chongqing Emergency Medical Center, Chongqing, China.
Front Med (Lausanne). 2024 Sep 20;11:1400166. doi: 10.3389/fmed.2024.1400166. eCollection 2024.
Sepsis poses a serious threat to individual life and health. Early and accessible diagnosis and targeted treatment are crucial. This study aims to explore the relationship between microbes, metabolic pathways, and blood test indicators in sepsis patients and develop a machine learning model for clinical diagnosis.
Blood samples from sepsis patients were sequenced. α-diversity and β-diversity analyses were performed to compare the microbial diversity between the sepsis group and the normal group. Correlation analysis was conducted on microbes, metabolic pathways, and blood test indicators. In addition, a model was developed based on medical records and radiomic features using machine learning algorithms.
The results of α-diversity and β-diversity analyses showed that the microbial diversity of sepsis group was significantly higher than that of normal group ( < 0.05). The top 10 microbial abundances in the sepsis and normal groups were Vitis vinifera, , and . The enriched metabolic pathways mainly included Protein families: genetic information processing, Translation, Protein families: signaling and cellular processes, and Unclassified: genetic information processing. The correlation analysis revealed a significant positive correlation ( < 0.05) between IL-6 and Membrane transport. Metabolism of other amino acids showed a significant positive correlation ( < 0.05) with , and . Ananas comosus showed a significant positive correlation ( < 0.05) with Poorly characterized and Unclassified: metabolism. Blood test-related indicators showed a significant negative correlation ( < 0.05) with microorganisms. Logistic regression (LR) was used as the optimal model in six machine learning models based on medical records and radiomic features. The nomogram, calibration curves, and AUC values demonstrated that LR performed best for prediction.
This study provides insights into the relationship between microbes, metabolic pathways, and blood test indicators in sepsis. The developed machine learning model shows potential for aiding in clinical diagnosis. However, further research is needed to validate and improve the model.
脓毒症对个体生命和健康构成严重威胁。早期且可及的诊断以及针对性治疗至关重要。本研究旨在探讨脓毒症患者中微生物、代谢途径和血液检测指标之间的关系，并开发一种用于临床诊断的机器学习模型。
对脓毒症患者的血液样本进行测序。进行α多样性和β多样性分析以比较脓毒症组和正常组之间的微生物多样性。对微生物、代谢途径和血液检测指标进行相关性分析。此外，使用机器学习算法基于病历和放射组学特征开发了一个模型。
α多样性和β多样性分析结果表明，脓毒症组的微生物多样性显著高于正常组（<0.05）。脓毒症组和正常组中前10位的微生物丰度分别是葡萄、 、 。富集的代谢途径主要包括蛋白质家族：遗传信息处理、翻译、蛋白质家族：信号传导和细胞过程以及未分类：遗传信息处理。相关性分析显示白细胞介素-6与膜转运之间存在显著正相关（<0.05）。其他氨基酸代谢与 、 和 之间存在显著正相关（<0.05）。菠萝与特征不明确和未分类：代谢之间存在显著正相关（<0.05）。血液检测相关指标与微生物之间存在显著负相关（<0.05）。在基于病历和放射组学特征的六个机器学习模型中，逻辑回归（LR）被用作最佳模型。列线图、校准曲线和AUC值表明LR在预测方面表现最佳。
本研究为脓毒症中微生物、代谢途径和血液检测指标之间的关系提供了见解。所开发的机器学习模型显示出辅助临床诊断的潜力。然而，需要进一步研究来验证和改进该模型。
