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本文引用的文献

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Deep Remission at 1 Year Prevents Progression of Early Crohn's Disease.1 年深度缓解可预防早期克罗恩病进展。
Gastroenterology. 2020 Jul;159(1):139-147. doi: 10.1053/j.gastro.2020.03.039. Epub 2020 Mar 26.
2
Single-Cell Analysis of Crohn's Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy.单细胞分析克罗恩病病变可鉴定与抗 TNF 治疗抵抗相关的致病细胞模块。
Cell. 2019 Sep 5;178(6):1493-1508.e20. doi: 10.1016/j.cell.2019.08.008. Epub 2019 Aug 29.
3
Serum Protein Biomarkers of Fibrosis Aid in Risk Stratification of Future Stricturing Complications in Pediatric Crohn's Disease.血清纤维化蛋白生物标志物有助于对儿童克罗恩病未来的狭窄性并发症进行风险分层。
Am J Gastroenterol. 2019 May;114(5):777-785. doi: 10.14309/ajg.0000000000000237.
4
A blood-based prognostic biomarker in IBD.炎症性肠病的一种基于血液的预后生物标志物。
Gut. 2019 Aug;68(8):1386-1395. doi: 10.1136/gutjnl-2019-318343. Epub 2019 Apr 27.
5
Differential Intestinal Mucosa Transcriptomic Biomarkers for Crohn's Disease and Ulcerative Colitis.克罗恩病和溃疡性结肠炎的肠道黏膜差异转录组学生物标志物。
J Immunol Res. 2018 Oct 17;2018:9208274. doi: 10.1155/2018/9208274. eCollection 2018.
6
ACG Clinical Guideline: Management of Crohn's Disease in Adults.ACG 临床指南:成人克罗恩病的管理。
Am J Gastroenterol. 2018 Apr;113(4):481-517. doi: 10.1038/ajg.2018.27. Epub 2018 Mar 27.
7
Effect of tight control management on Crohn's disease (CALM): a multicentre, randomised, controlled phase 3 trial.紧密控制管理对克罗恩病(CALM)的影响:一项多中心、随机、对照的 3 期临床试验。
Lancet. 2017 Dec 23;390(10114):2779-2789. doi: 10.1016/S0140-6736(17)32641-7. Epub 2017 Oct 31.
8
Prediction of complicated disease course for children newly diagnosed with Crohn's disease: a multicentre inception cohort study.新诊断为克罗恩病儿童复杂疾病病程的预测:一项多中心发病队列研究。
Lancet. 2017 Apr 29;389(10080):1710-1718. doi: 10.1016/S0140-6736(17)30317-3. Epub 2017 Mar 2.
9
Crohn's disease.克罗恩病。
Lancet. 2017 Apr 29;389(10080):1741-1755. doi: 10.1016/S0140-6736(16)31711-1. Epub 2016 Dec 1.
10
Employment status, difficulties at work and quality of life in inflammatory bowel disease patients.炎症性肠病患者的就业状况、工作困难及生活质量
Eur J Gastroenterol Hepatol. 2016 Oct;28(10):1130-6. doi: 10.1097/MEG.0000000000000685.

机器学习识别新诊断的小儿克罗恩病并发穿透性和狭窄性并发症的新型血液蛋白预测因子。

Machine learning identifies novel blood protein predictors of penetrating and stricturing complications in newly diagnosed paediatric Crohn's disease.

机构信息

The Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

出版信息

Aliment Pharmacol Ther. 2021 Jan;53(2):281-290. doi: 10.1111/apt.16136. Epub 2020 Nov 1.

DOI:10.1111/apt.16136
PMID:33131065
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7770008/
Abstract

BACKGROUND

There is a need for improved risk stratification in Crohn's disease.

AIM

To identify novel blood protein biomarkers associated with future Crohn's disease complications METHODS: We performed a case-cohort study utilising a paediatric inception cohort, the Risk Stratification and Identification of Immunogenetic and Microbial Markers of Rapid Disease Progression in Children with Crohn's disease (RISK) study. All patients had inflammatory disease (B1) at baseline. Outcomes were development of stricturing (B2) or penetrating (B3) complications. We assayed 92 inflammation-related proteins in baseline plasma using a proximity extension assay (Olink Proteomics). An ensemble machine learning technique, random survival forests (RSF), selected variables predicting B2 and B3 complications. Selected analytes were compared to clinical variables and serology only models. We examined selected proteins in a single-cell sequencing cohort to analyse differential cell expression in blood and ileum.

RESULTS

We included 265 patients with mean age 11.6 years (standard deviation [SD] 3.2). Seventy-three and 34 patients, respectively, had B2 and B3 complications within mean 1123 (SD 477) days for B2 and 1251 (442) for B3. A model with 5 protein markers predicted B3 complications with an area under the curve (AUC) of 0.79 (95% confidence interval [CI] 0.76-0.82) compared to 0.69 (95% CI 0.66-0.72) for serologies and 0.74 (95% CI 0.71-0.77) for clinical variables. A model with 4 protein markers predicted B2 complications with an AUC of 0.68 (95% CI 0.65-0.71) compared to 0.62 (95% CI 0.59-0.65) for serologies and 0.52 (95% CI 0.50-0.55) for clinical variables. B2 analytes were highly expressed in ileal stromal cells while B3 analytes were prominent in peripheral blood and ileal T cells.

CONCLUSIONS

We identified novel blood proteomic markers, distinct for B2 and B3, associated with progression of paediatric Crohn's disease.

摘要

背景

克罗恩病的风险分层需要改进。

目的

确定与克罗恩病未来并发症相关的新的血液蛋白生物标志物。

方法

我们利用儿科发病队列进行了病例对照研究,即风险分层和识别儿童克罗恩病快速疾病进展的免疫遗传和微生物标志物研究(RISK)。所有患者在基线时均患有炎症性疾病(B1)。结局为狭窄(B2)或穿透(B3)并发症的发展。我们使用邻近延伸测定法(Olink Proteomics)测定基线血浆中 92 种炎症相关蛋白。随机生存森林(RSF)的集成机器学习技术选择预测 B2 和 B3 并发症的变量。将选定的分析物与临床变量和血清学仅模型进行比较。我们在单细胞测序队列中检查了选定的蛋白质,以分析血液和回肠中差异细胞表达。

结果

我们纳入了 265 例平均年龄为 11.6 岁(标准差 [SD] 3.2)的患者。分别有 73 例和 34 例患者在平均 1123 天(SD 477)内发生 B2 并发症,在平均 1251 天(SD 442)内发生 B3 并发症。一个包含 5 种蛋白标志物的模型预测 B3 并发症的曲线下面积(AUC)为 0.79(95%置信区间 [CI] 0.76-0.82),与血清学的 0.69(95% CI 0.66-0.72)和临床变量的 0.74(95% CI 0.71-0.77)相比。一个包含 4 种蛋白标志物的模型预测 B2 并发症的 AUC 为 0.68(95% CI 0.65-0.71),与血清学的 0.62(95% CI 0.59-0.65)和临床变量的 0.52(95% CI 0.50-0.55)相比。B2 分析物在回肠基质细胞中高表达,而 B3 分析物在周围血液和回肠 T 细胞中突出。

结论

我们鉴定了与儿童克罗恩病进展相关的新的血液蛋白质生物标志物,这些标志物在 B2 和 B3 中是不同的。