P144 Changes of fecal metabolic and lipidomic features by anti-tumor necrosis factor treatment and prediction of clinical remission in patients with ulcerative colitis. (21st January 2022) - Shin Shin, S; Kim, S Y; Park, S J; Im, J P; Kim, H J; Lee, K M; Kim, J W; Jung, S A; Lee, J; Kang, S B; Shin, S J; Kim, E S; et al.
Abstract: Background: Tumor necrosis factor (TNF) antagonists are recommended for patients with ulcerative colitis (UC) for the effectiveness in inducing and maintaining clinical remission. We investigated the altered fecal metabolites and lipids by anti-TNF treatment and prediction model of remission in patients with UC. Methods: A prospective, observational multicenter study was conducted at 17 academic hospitals in Korea. Fecal samples were collected from adult patients with moderately to severely active UC (n=116) before and after 8 and 56 weeks of adalimumab treatment and from healthy controls (HC, n=37). Clinical remission was assessed using Mayo score. Metabolome and lipidome analyses were performed using gas chromatography-, and nano electro spray ionization-mass spectrometry, respectively. Prediction models of remission were developed using baseline fecal samples by Fourier transform-infrared (FT-IR) spectroscopy combined with machine learning algorithms. Results: Fecal metabolites and lipids in UC were different from HC at baseline and were changed similarly to HC during treatment. Fecal metabolites and lipids in remitters (RM) after treatment were more grouped and clustered with those of HC compared with non-remitters (NRM). In RM, 2-aminobutyric acid, galactose and dodecanoate levels which were previously decreased at baseline compared to HC increased to the levels of HC, whereas benzoate, stigmasterol, 3-hydroxybutyrate, diacylglycerol and triacylglycerol levels which were previously increased at baseline compared to HC decreased to the levels of HC after 56 weeks of treatment. The best model predicting short-term remission was developed by applying logistic regression (LR) and radial basis functions (rbf) support vector machine (SVM) with an accuracy of 0.99 (95% confidence interval [CI], 0.98–1.01). For long-term remission, the best prediction model was developed by rbf-SVM revealing 0.99 [CI 0.98–1.01]. LR and K-nearest neighbors also showed excellent performance for prediction of long-term remission (accuracy of 0.96 [CI 0.90–1.02] and 0.96 [CI 0.92–1.00], respectively. Conclusion: Fecal characteristics in UC were changed after anti-TNF treatment and became similar to those of HC. Potential therapeutic target compounds were suggested to develop novel therapeutic strategies for UC. Novel remission prediction models by FT-IR spectroscopy were also established.