Grännö Olle, Bergemalm Daniel, Salomon Benita, Lindqvist Carl Mårten, Hedin Charlotte R H, Carlson Marie, Dannenberg Katharina, Andersson Erik, Keita Åsa V, Magnusson Maria K, Eriksson Carl, Lanka Vivekananda, Magnusson Patrik K E, D'Amato Mauro, Öhman Lena, Söderholm Johan D, Hultdin Johan, Kruse Robert, Cao Yang, Repsilber Dirk, Grip Olof, Karling Pontus, Halfvarson Jonas
Department of Laboratory Medicine, Clinical Microbiology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
Department of Gastroenterology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
Gastroenterology. 2025 Apr;168(4):741-753. doi: 10.1053/j.gastro.2024.11.006. Epub 2024 Nov 26.
BACKGROUND & AIMS: Biomarkers are needed to identify individuals at elevated risk of inflammatory bowel disease. This study aimed to identify protein signatures predictive of inflammatory bowel disease.
Using large population-based cohorts (n ≥180,000), blood samples were obtained from individuals who later in life were diagnosed with inflammatory bowel disease and compared with age and sex-matched controls, free from inflammatory bowel disease during follow-up. A total of 178 proteins were measured on Olink platforms. We used machine-learning methods to identify protein signatures of preclinical disease in the discovery cohort (n = 312). Their performance was validated in an external preclinical cohort (n = 222) and assessed in an inception cohort (n = 144) and a preclinical twin cohort (n = 102).
In the discovery cohort, a signature of 29 proteins differentiated preclinical Crohn's disease (CD) cases from controls, with an area under the curve (AUC) of 0.85. Its performance was confirmed in the preclinical validation (AUC = 0.87) and the inception cohort (AUC = 1.0). In preclinical samples, downregulated (but not upregulated) proteins related to gut barrier integrity and macrophage functionality correlated with time to diagnosis of CD. The preclinical ulcerative colitis signature had a significant, albeit lower, predictive ability in the discovery (AUC = 0.77), validation (AUC = 0.67), and inception cohorts (AUC = 0.95). The preclinical signature for CD demonstrated an AUC of 0.89 when comparing twins with preclinical CD with matched external healthy twins, but its predictive ability was lower (AUC = 0.58; P = .04) when comparing them with their healthy twin siblings, that is, when accounting for genetic and shared environmental factors.
We identified protein signatures for predicting a future diagnosis of CD and ulcerative colitis, validated across independent cohorts. In the context of CD, the signature offers potential for early prediction.
需要生物标志物来识别炎症性肠病风险升高的个体。本研究旨在识别可预测炎症性肠病的蛋白质特征。
利用基于人群的大型队列（n≥180,000），从后来被诊断为炎症性肠病的个体中采集血样，并与年龄和性别匹配的对照组进行比较，这些对照组在随访期间未患炎症性肠病。在Olink平台上共检测了178种蛋白质。我们使用机器学习方法在发现队列（n = 312）中识别临床前疾病的蛋白质特征。其性能在外部临床前队列（n = 222）中得到验证，并在起始队列（n = 144）和临床前双胞胎队列（n = 102）中进行评估。
在发现队列中，一个由29种蛋白质组成的特征可将临床前克罗恩病（CD）病例与对照组区分开来，曲线下面积（AUC）为0.85。其性能在临床前验证队列（AUC = 0.87）和起始队列（AUC = 1.0）中得到证实。在临床前样本中，与肠道屏障完整性和巨噬细胞功能相关的下调（而非上调）蛋白质与CD诊断时间相关。临床前溃疡性结肠炎特征在发现队列（AUC = 0.77）、验证队列（AUC = 0.67）和起始队列（AUC = 0.95）中具有显著但较低的预测能力。当将临床前CD双胞胎与匹配的外部健康双胞胎进行比较时，CD的临床前特征的AUC为0.89，但当将他们与其健康的双胞胎兄弟姐妹进行比较时，即考虑遗传和共同环境因素时，其预测能力较低（AUC = 0.58；P = 0.04）。
我们识别出了可预测未来CD和溃疡性结肠炎诊断的蛋白质特征，并在独立队列中得到验证。在CD的背景下，该特征具有早期预测的潜力。
