The nasal microbiota is a potential diagnostic biomarker for sepsis in critical care units

被引:1
作者
Tan, Xilan [1 ,2 ]
Liu, Haiyue [3 ]
Qiu, Wen [4 ]
Li, Zewen [4 ]
Ge, Shuang [4 ]
Luo, Yuemei [2 ]
Zeng, Nianyi [4 ]
Chen, Manjun [4 ]
Zhou, Qiqi [4 ]
Cai, Shumin [5 ]
Long, Jun [4 ]
Cen, Zhongran [6 ]
Su, Jin [7 ]
Zhou, Hongwei [4 ]
He, Xiaolong [4 ]
机构
[1] Southern Med Univ, Zhujiang Hosp, Div Lab Med, Guangzhou, Peoples R China
[2] Southern Med Univ, Zhujiang Hosp, Div Lab Med, State Key Lab Organ Failure Res,Microbiome Med Ct, Guangzhou, Peoples R China
[3] Xiamen Univ, Xiamen Key Lab Genet Testing, Sch Med, Dept Lab Med,Affiliated Hosp 1, Xiamen, Peoples R China
[4] Southern Med Univ, Zhujiang Hosp, Microbiome Med Ctr, Dept Lab Med, Guangzhou, Peoples R China
[5] Southern Med Univ, Nanfang Hosp, Dept Intens Care Med, Guagnzhou, Peoples R China
[6] Southern Med Univ, Zhujiang Hosp, Div Intens Care Med, Guangzhou, Peoples R China
[7] Southern Med Univ, Nanfang Hosp, Dept Resp & Crit Care Med, Chron Airways Dis Lab, Guangzhou, Peoples R China
来源
MICROBIOLOGY SPECTRUM | 2024年 / 12卷 / 07期
基金
中国国家自然科学基金;
关键词
sepsis; nasal microbiota; gut microbiota; 16S rRNA; SEPTIC SHOCK; ANTIBIOTICS; PATHOBIOME;
D O I
10.1128/spectrum.03441-23
中图分类号
Q93 [微生物学];
学科分类号
071005 ; 100705 ;
摘要
This study aimed to characterize the composition of intestinal and nasal microbiota in septic patients and identify potential microbial biomarkers for diagnosis. A total of 157 subjects, including 89 with sepsis, were enrolled from the affiliated hospital. Nasal swabs and fecal specimens were collected from septic and non-septic patients in the intensive care unit (ICU) and Department of Respiratory and Critical Care Medicine. DNA was extracted, and the V4 region of the 16S rRNA gene was amplified and sequenced using Illumina technology. Bioinformatics analysis, statistical processing, and machine learning techniques were employed to differentiate between septic and non-septic patients. The nasal microbiota of septic patients exhibited significantly lower community richness (P = 0.002) and distinct compositions (P = 0.001) compared to non-septic patients. Corynebacterium, Staphylococcus, Acinetobacter, and Pseudomonas were identified as enriched genera in the nasal microbiota of septic patients. The constructed machine learning model achieved an area under the curve (AUC) of 89.08, indicating its efficacy in differentiating septic and non-septic patients. Importantly, model validation demonstrated the effectiveness of the nasal microecological diagnosis prediction model with an AUC of 84.79, while the gut microecological diagnosis prediction model had poor predictive performance (AUC = 49.24). The nasal microbiota of ICU patients effectively distinguishes sepsis from non-septic cases and outperforms the gut microbiota. These findings have implications for the development of diagnostic strategies and advancements in critical care medicine. IMPORTANCE The important clinical significance of this study is that it compared the intestinal and nasal microbiota of sepsis with non-sepsis patients and determined that the nasal microbiota is more effective than the intestinal microbiota in distinguishing patients with sepsis from those without sepsis, based on the difference in the lines of nasal specimens collected. The important clinical significance of this study is that it compared the intestinal and nasal microbiota of sepsis with non-sepsis patients and determined that the nasal microbiota is more effective than the intestinal microbiota in distinguishing patients with sepsis from those without sepsis, based on the difference in the lines of nasal specimens collected.
引用
收藏
页数:11
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