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。