Survival of the Littlest: Navigating Sepsis Diagnosis beyond Inflammation in Preterm Neonates.

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Manchu Umarani Thangavelu, Alida Kindt, Shawen Hassan, Jelte J B Geerlings, Charlotte Nijgh-van Kooij, Irwin K M Reiss, Bert Wouters, H Rob Taal, Thomas Hankemeier
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引用次数: 0

Abstract

Sepsis diagnosis in preterm neonates is challenging due to symptom overlap with non-infectious inflammatory conditions, and slow, unreliable diagnostic practices. This case-control study aims to elucidate sepsis pathophysiology, and identify metabolic biomarkers for timely, accurate diagnosis, to prevent rapid health deterioration and unnecessary antibiotic use. Liquid chromatography-mass spectrometry was performed on 227 plasma samples, obtained from 94 preterm neonates, to measure 317 metabolites encompassing amines and signaling lipids. Linear mixed-effect modeling, LASSO and logistic regression models were calculated to assess metabolic alterations across control, systemic inflammation-no sepsis (SINS), and sepsis groups. Stratification by sex and pathogen type allowed identification of sex-specific responses and pathogen-driven variations in sepsis. Key findings include (i) shared metabolic changes in SINS and sepsis, (ii) progressive alterations from control to SINS to sepsis, and (iii) sepsis-specific markers. Males exhibited a pro-inflammatory phenotype while females showed an anti-inflammatory phenotype in response to sepsis. Gram-positive and gram-negative bacterial sepsis revealed distinct metabolic profiles. A diagnostic model comprising 5 metabolic features and IL-6 distinguished SINS from sepsis at clinical suspicion (AUC 0.79, sensitivity 0.85, specificity 0.82). These insights highlight the potential of metabolomics to revolutionize neonatal sepsis management with precision diagnostics and personalized treatment strategies.

最小的生存:导航败血症诊断超越炎症在早产儿。
由于症状与非感染性炎症条件重叠,以及缓慢、不可靠的诊断方法,早产新生儿败血症的诊断具有挑战性。本病例对照研究旨在阐明脓毒症的病理生理,识别代谢生物标志物,以便及时、准确诊断,防止健康迅速恶化和不必要的抗生素使用。对94名早产儿的227份血浆样本进行了液相色谱-质谱分析,测量了包括胺和信号脂质的317种代谢物。计算线性混合效应模型、LASSO和逻辑回归模型来评估对照组、系统性炎症-无脓毒症(SINS)组和脓毒症组的代谢变化。性别和病原体类型的分层允许识别性别特异性反应和病原体驱动的脓毒症变异。主要发现包括(i) SINS和败血症的共同代谢变化,(ii)从对照组到SINS到败血症的进行性改变,以及(iii)败血症特异性标志物。在脓毒症反应中,雄性表现出促炎表型,而雌性表现出抗炎表型。革兰氏阳性和革兰氏阴性细菌性败血症显示出不同的代谢谱。一个包含5个代谢特征和IL-6的诊断模型在临床怀疑时将SINS与败血症区分出来(AUC 0.79,敏感性0.85,特异性0.82)。这些见解突出了代谢组学通过精确诊断和个性化治疗策略彻底改变新生儿败血症管理的潜力。
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
Journal of Proteome Research
Journal of Proteome Research 生物-生化研究方法
CiteScore
9.00
自引率
4.50%
发文量
251
审稿时长
3 months
期刊介绍: Journal of Proteome Research publishes content encompassing all aspects of global protein analysis and function, including the dynamic aspects of genomics, spatio-temporal proteomics, metabonomics and metabolomics, clinical and agricultural proteomics, as well as advances in methodology including bioinformatics. The theme and emphasis is on a multidisciplinary approach to the life sciences through the synergy between the different types of "omics".
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