Monitoring of intestinal inflammation and prediction of recurrence in ulcerative colitis

Abstract

Background and objectives: Ulcerative colitis is a chronic recurrent intestinal inflammatory disease, and its recurrence is difficult to predict. In this review, we summarized the objective indicators that can be used to evaluate intestinal inflammation, the purpose is to better predict the clinical recurrence of UC, formulate individualized treatment plan during remission of UC, and improve the level of diagnosis and treatment of UC.

Methods: Based on the search results in the PUBMED database, we explored the accuracy and value of these methods in predicting the clinical recurrence of UC from the following three aspects: endoscopic and histological scores, serum biomarkers and fecal biomarkers.

Results: Colonoscopy with biopsy is the gold standard for assessing intestinal inflammation, but it is invasive, inconvenient and expensive. At present, there is no highly sensitive and specific endoscopic or histological score to predict the clinical recurrence of UC. Compared with serum biomarkers, fecal biomarkers have higher sensitivity and specificity because they are in direct contact with the intestine and are closer to the site of intestinal inflammation. Fecal calprotectin is currently the most studied and meaningful fecal biomarker. Lactoferrin and S100A12, as novel biomarkers, have no better performance than FC in predicting the recurrence of UC.

Conclusions: FC is currently the most promising predictive marker, but it lacks an accurate cut-off value. Combining patient symptoms, incorporating multiple indicators to construct a UC recurrence prediction model, and formulating individualized treatment plans for high recurrence risk patients will be the focus of UC remission management.

Keywords:

• Ulcerative colitis
• recurrence prediction
• inflammatory monitoring
• calprotectin

Author contributions

Changchang Ge contributes to the acquisition, preparation and interpretation of the data, and drafted most of this article. Yi Lu also contributed to data acquisition, preparation, and interpretation and a second main contribution to a manuscript. Lei Zhu, Hong Shen has been involved in the revision of the manual script, is critical to important knowledge content. All authors read and approve the final manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data supporting the conclusions of this article is included within the article.

Additional information

Funding

The present study was supported by the National Key R&D Program [No. 2017YFC1700104].