Division of Infection Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
State Key Laboratory of Organ Failure Research, Division of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Microbiol Spectr. 2024 Jul 2;12(7):e0344123. doi: 10.1128/spectrum.03441-23. Epub 2024 Jun 12.
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 ( = 0.002) and distinct compositions ( = 0.001) compared to non-septic patients. , , , and 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.IMPORTANCEThe 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.
本研究旨在描述脓毒症患者肠道和鼻腔微生物群落的组成，并鉴定潜在的微生物生物标志物用于诊断。共纳入了 157 名患者，其中 89 名为脓毒症患者，均来自附属医院。采集 ICU 和呼吸与危重症医学科的脓毒症和非脓毒症患者的鼻腔拭子和粪便标本。提取 DNA，使用 Illumina 技术扩增和测序 16S rRNA 基因 V4 区。采用生物信息学分析、统计处理和机器学习技术来区分脓毒症和非脓毒症患者。与非脓毒症患者相比，脓毒症患者的鼻腔微生物群落丰富度显著降低（ = 0.002），且组成明显不同（ = 0.001）。在脓毒症患者的鼻腔微生物群落中， 、 、 、 和 被鉴定为富集菌属。构建的机器学习模型的 AUC 为 89.08，表明其在区分脓毒症和非脓毒症患者方面具有较好的效果。重要的是，模型验证表明，鼻腔微生态诊断预测模型的有效性良好，AUC 为 84.79，而肠道微生态诊断预测模型的预测性能较差（AUC = 49.24）。ICU 患者的鼻腔微生物群落能够有效地将脓毒症与非脓毒症病例区分开来，其性能优于肠道微生物群落。这些发现对于开发诊断策略和推动重症医学的发展具有重要意义。
重要性
本研究的重要临床意义在于，通过比较脓毒症和非脓毒症患者的肠道和鼻腔微生物群落，发现基于鼻腔标本采集线的不同，鼻腔微生物群落比肠道微生物群落更有效地将脓毒症患者与非脓毒症患者区分开来。
