Sokollik Christiane, Pahud de Mortanges Aurélie, Leichtle Alexander B, Juillerat Pascal, Horn Michael P
Division of Pediatric Gastroenterology, Hepatology and Nutrition, University Children's Hospital, Inselspital, University of Bern, 3010 Bern, Switzerland.
ARTORG Center for Biomedical Engineering Research, University of Bern, 3010 Bern, Switzerland.
Diagnostics (Basel). 2023 Jul 26;13(15):2491. doi: 10.3390/diagnostics13152491.
Antibody testing in inflammatory bowel disease (IBD) can add to diagnostic accuracy of the main subtypes Crohn's disease (CD) and ulcerative colitis (UC). Whether modern modeling techniques such as supervised and unsupervised machine learning are of value for finer distinction of subtypes such as IBD-unclassified (IBD-U) is not known. We determined the antibody profile of 100 adult IBD patients from the Swiss IBD cohort study with known subtype (50 CD, 50 UC) as well as of 76 IBD-U patients. We included ASCA IgG and IgA, p-ANCA, MPO- and PR3-ANCA, and xANCA measurements for computing different antibody panels as well as machine learning models. The AUC of an optimized antibody panel was 85% (95%CI, 78-92%) to distinguish CD from UC patients. The antibody profile of IBD-U patients was closely related to UC. No specific antibody profile was predictive for IBD-U nor for re-classification. The panel diagnostic was in favor of UC reclassification prediction with a correct assignment rate of 69.2-73.1% depending on the cut-off applied. Supervised machine learning could not distinguish between CD, UC, and IBD-U. More so, unsupervised machine learning suggested only two distinct clusters as a likely number of IBD subtypes. Antibodies in IBD are supportive in confirming clinical determined subtypes CD and UC but have limited capacity to predict IBD-U and reclassification during follow-up. In terms of antibody profiles, IBD-U is not a distinct subtype of IBD.
炎症性肠病（IBD）中的抗体检测可提高主要亚型克罗恩病（CD）和溃疡性结肠炎（UC）的诊断准确性。目前尚不清楚诸如监督式和非监督式机器学习等现代建模技术对于更精细地区分IBD未分类（IBD-U）等亚型是否有价值。我们从瑞士IBD队列研究中确定了100名已知亚型（50例CD，50例UC）的成年IBD患者以及76例IBD-U患者血清中的抗体谱。我们纳入了抗酿酒酵母抗体IgG和IgA、核周型抗中性粒细胞胞浆抗体（p-ANCA）、髓过氧化物酶抗中性粒细胞胞浆抗体（MPO-ANCA）和蛋白酶3抗中性粒细胞胞浆抗体（PR3-ANCA）以及xANCA测量值，以计算不同的抗体组合以及机器学习模型。优化后的抗体组合区分CD患者和UC患者的曲线下面积（AUC）为85%（95%置信区间，78-92%）。IBD-U患者的抗体谱与UC密切相关。没有特定的抗体谱可预测IBD-U或重新分类。根据应用的临界值，该组合诊断有利于UC重新分类预测，正确分配率为69.2-73.1%。监督式机器学习无法区分CD、UC和IBD-U。更确切地说，非监督式机器学习表明IBD亚型可能只有两个不同的聚类。IBD中的抗体有助于确认临床确定的亚型CD和UC，但在随访期间预测IBD-U和重新分类的能力有限。就抗体谱而言，IBD-U不是IBD的一个独特亚型。
