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基于体温轨迹的脓毒症亚表型的不同免疫特征和临床结局。

Distinct immune profiles and clinical outcomes in sepsis subphenotypes based on temperature trajectories.

机构信息

School of Medicine, Emory University, Atlanta, GA, USA.

Emory Critical Care Center, Atlanta, GA, USA.

出版信息

Intensive Care Med. 2024 Dec;50(12):2094-2104. doi: 10.1007/s00134-024-07669-0. Epub 2024 Oct 9.

DOI:10.1007/s00134-024-07669-0
PMID:39382693
Abstract

PURPOSE

Sepsis is a heterogeneous syndrome. Identification of sepsis subphenotypes with distinct immune profiles could lead to targeted therapies. This study investigates the immune profiles of patients with sepsis following distinct body temperature patterns (i.e., temperature trajectory subphenotypes).

METHODS

Hospitalized patients from four hospitals between 2018 and 2022 with suspicion of infection were included. A previously validated temperature trajectory algorithm was used to classify study patients into temperature trajectory subphenotypes. Microbiological profiles, clinical outcomes, and levels of 31 biomarkers were compared between these subphenotypes.

RESULTS

The 3576 study patients were classified into four temperature trajectory subphenotypes: hyperthermic slow resolvers (N = 563, 16%), hyperthermic fast resolvers (N = 805, 23%), normothermic (N = 1693, 47%), hypothermic (N = 515, 14%). The mortality rate was significantly different between subphenotypes, with the highest rate in hypothermics (14.2%), followed by hyperthermic slow resolvers 6%, normothermic 5.5%, and lowest in hyperthermic fast resolvers 3.6% (p < 0.001). After multiple testing correction for the 31 biomarkers tested, 20 biomarkers remained significantly different between temperature trajectories: angiopoietin-1 (Ang-1), C-reactive protein (CRP), feline McDonough sarcoma-like tyrosine kinase 3 ligand (Flt-3l), granulocyte colony stimulating factor (G-CSF), granulocyte-macrophage colony stimulating factor (GM-CSF), interleukin (IL)-15, IL-1 receptor antagonist (RA), IL-2, IL-6, IL-7, interferon gamma-induced protein 10 (IP-10), monocyte chemoattractant protein-1 (MCP-1), human macrophage inflammatory protein 3 alpha (MIP-3a), neutrophil gelatinase-associated lipocalin (NGAL), pentraxin-3, thrombomodulin, tissue factor, soluble triggering receptor expressed on myeloid cells-1 (sTREM-1), and vascular cellular adhesion molecule-1 (vCAM-1).The hyperthermic fast and slow resolvers had the highest levels of most pro- and anti-inflammatory cytokines. Hypothermics had suppressed levels of most cytokines but the highest levels of several coagulation markers (Ang-1, thrombomodulin, tissue factor).

CONCLUSION

Sepsis subphenotypes identified using the universally available measurement of body temperature had distinct immune profiles. Hypothermic patients, who had the highest mortality rate, also had the lowest levels of most pro- and anti-inflammatory cytokines.

摘要

目的

脓毒症是一种异质性综合征。识别具有不同免疫特征的脓毒症亚表型可能会导致靶向治疗。本研究调查了具有不同体温模式(即体温轨迹亚表型)的脓毒症患者的免疫特征。

方法

纳入了 2018 年至 2022 年间来自四家医院的疑似感染的住院患者。使用先前验证的体温轨迹算法将研究患者分为体温轨迹亚表型。比较这些亚表型之间的微生物谱、临床结局和 31 种生物标志物的水平。

结果

3576 例研究患者被分为四种体温轨迹亚表型:高热缓慢缓解者(N=563,16%)、高热快速缓解者(N=805,23%)、正常体温者(N=1693,47%)和低温者(N=515,14%)。各亚表型之间的死亡率有显著差异,低温者的死亡率最高(14.2%),其次是高热缓慢缓解者 6%、正常体温者 5.5%和高热快速缓解者最低 3.6%(p<0.001)。对 31 种经过测试的生物标志物进行多重测试校正后,有 20 种标志物在体温轨迹之间仍存在显著差异:血管生成素-1(Ang-1)、C 反应蛋白(CRP)、猫麦克唐纳肉瘤样酪氨酸激酶 3 配体(Flt-3l)、粒细胞集落刺激因子(G-CSF)、粒细胞-巨噬细胞集落刺激因子(GM-CSF)、白细胞介素(IL)-15、IL-1 受体拮抗剂(IL-1RA)、IL-2、IL-6、IL-7、干扰素γ诱导蛋白 10(IP-10)、单核细胞趋化蛋白-1(MCP-1)、人巨噬细胞炎症蛋白 3α(MIP-3a)、中性粒细胞明胶酶相关脂质运载蛋白(NGAL)、Pentraxin-3、血栓调节蛋白、组织因子、髓样细胞表达的触发受体-1(sTREM-1)和血管细胞黏附分子-1(VCAM-1)。高热快速和缓慢缓解者具有大多数促炎和抗炎细胞因子的最高水平。低温者的大多数细胞因子水平受到抑制,但几种凝血标志物(Ang-1、血栓调节蛋白、组织因子)水平最高。

结论

使用普遍可测量的体温来识别脓毒症亚表型具有不同的免疫特征。体温最低的低温患者死亡率最高,同时也具有大多数促炎和抗炎细胞因子的最低水平。

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2
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Crit Care Med. 2023 Dec 1;51(12):1697-1705. doi: 10.1097/CCM.0000000000005983. Epub 2023 Jun 28.
3
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Intensive Care Med. 2025 Sep;51(9):1699-1702. doi: 10.1007/s00134-025-08061-2. Epub 2025 Aug 19.
4
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Front Pediatr. 2025 Jul 18;13:1599694. doi: 10.3389/fped.2025.1599694. eCollection 2025.
5
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Cytojournal. 2025 Apr 25;22:46. doi: 10.25259/Cytojournal_253_2024. eCollection 2025.
6
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Front Immunol. 2025 Feb 6;16:1546474. doi: 10.3389/fimmu.2025.1546474. eCollection 2025.
7
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8
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Prospective evaluation of the efficacy, safety, and optimal biomarker enrichment strategy for nangibotide, a TREM-1 inhibitor, in patients with septic shock (ASTONISH): a double-blind, randomised, controlled, phase 2b trial.
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4
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