Targeted urinary metabolomics combined with machine learning to identify biomarkers related to central carbon metabolism for IBD
IntroductionInflammatory bowel disease (IBD), comprising Crohn’s disease (CD) and ulcerative colitis (UC), is a chronic and relapsing inflammatory disorder of the gastrointestinal tract. Current diagnostic approaches are invasive, costly, and time-consuming, underscoring the need for non-invasive, a...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Molecular Biosciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2025.1615047/full |
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| author | Miao-Lin Lei Guan-Wei Bi Xiao-Lin Yin Xiao-Lin Yin Yue Wang Yue Wang Yue Wang Zi-Ru Sun Zi-Ru Sun Zi-Ru Sun Xin-rui Guo Hui-peng Zhang Xiao-han Zhao Feng Li Feng Li Yan-Bo Yu |
| author_facet | Miao-Lin Lei Guan-Wei Bi Xiao-Lin Yin Xiao-Lin Yin Yue Wang Yue Wang Yue Wang Zi-Ru Sun Zi-Ru Sun Zi-Ru Sun Xin-rui Guo Hui-peng Zhang Xiao-han Zhao Feng Li Feng Li Yan-Bo Yu |
| author_sort | Miao-Lin Lei |
| collection | DOAJ |
| description | IntroductionInflammatory bowel disease (IBD), comprising Crohn’s disease (CD) and ulcerative colitis (UC), is a chronic and relapsing inflammatory disorder of the gastrointestinal tract. Current diagnostic approaches are invasive, costly, and time-consuming, underscoring the need for non-invasive, accurate diagnostic methods.MethodsWe conducted a targeted metabolomic analysis of 49 metabolites related to central carbon metabolism in urinary samples from individuals with IBD and control group. Diagnostic models were constructed using six machine learning algorithms, and their performance was evaluated by cross-validated area under the receiver operating characteristic curve (AUC). The SHAP (SHapley Additive exPlanations) method was used to interpret the models and identify key discriminatory features.ResultsSix metabolites—xylose, isocitric acid, fructose, L-fucose, N-acetyl-D-glucosamine (GlcNAc), and glycolic acid—differentiated UC from control group, while three metabolites—xylose, L-fucose, and citric acid—distinguished CD from control group. The optimal diagnostic model achieved a mean AUC of 0.84 for UC and 0.93 for CD. These models retained high diagnostic accuracy even after adjusting for disease activity. SHAP analysis identified L-fucose, xylose, and GlcNAc as important features for UC, and citric acid and xylose for CD.DiscussionOur findings highlight distinct metabolic signatures in central carbon metabolism associated with IBD subtypes. The identified metabolite panels, combined with machine learning models, offer promising non-invasive tools for differentiating UC and CD from healthy individuals. |
| format | Article |
| id | doaj-art-a7dd058018404c4fa064fce207c7132d |
| institution | DOAJ |
| issn | 2296-889X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Molecular Biosciences |
| spelling | doaj-art-a7dd058018404c4fa064fce207c7132d2025-08-20T02:58:11ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2025-08-011210.3389/fmolb.2025.16150471615047Targeted urinary metabolomics combined with machine learning to identify biomarkers related to central carbon metabolism for IBDMiao-Lin Lei0Guan-Wei Bi1Xiao-Lin Yin2Xiao-Lin Yin3Yue Wang4Yue Wang5Yue Wang6Zi-Ru Sun7Zi-Ru Sun8Zi-Ru Sun9Xin-rui Guo10Hui-peng Zhang11Xiao-han Zhao12Feng Li13Feng Li14Yan-Bo Yu15Department of Gastroenterology, Qilu Hospital, Shandong University, Jinan, Shandong, ChinaDepartment of Gastroenterology, Qilu Hospital, Shandong University, Jinan, Shandong, ChinaDepartment of Gastroenterology, Qilu Hospital of Shandong University, Jinan, ChinaClinical Epidemiology Unit, Qilu Hospital of Shandong University, Jinan, ChinaNational Key Laboratory for Innovation and Transformation of Luobing Theory, Jinan, ChinaThe Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education, Chinese National Health Commission and Chinese Academy of Medical Sciences, Jinan, ChinaDepartment of Cardiology, Qilu Hospital of Shandong University, Jinan, ChinaNational Key Laboratory for Innovation and Transformation of Luobing Theory, Jinan, ChinaThe Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education, Chinese National Health Commission and Chinese Academy of Medical Sciences, Jinan, ChinaDepartment of Cardiology, Qilu Hospital of Shandong University, Jinan, ChinaDepartment of Gastroenterology, Qilu Hospital, Shandong University, Jinan, Shandong, ChinaDepartment of Gastroenterology, Qilu Hospital, Shandong University, Jinan, Shandong, ChinaDepartment of Gastroenterology, Qilu Hospital, Shandong University, Jinan, Shandong, ChinaDepartment of Pancreatic Surgery, General Surgery, Qilu Hospital of Shandong University, Jinan, ChinaDepartment of Gastroentero-Pancreatic Surgery, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, Shandong, ChinaDepartment of Gastroenterology, Qilu Hospital, Shandong University, Jinan, Shandong, ChinaIntroductionInflammatory bowel disease (IBD), comprising Crohn’s disease (CD) and ulcerative colitis (UC), is a chronic and relapsing inflammatory disorder of the gastrointestinal tract. Current diagnostic approaches are invasive, costly, and time-consuming, underscoring the need for non-invasive, accurate diagnostic methods.MethodsWe conducted a targeted metabolomic analysis of 49 metabolites related to central carbon metabolism in urinary samples from individuals with IBD and control group. Diagnostic models were constructed using six machine learning algorithms, and their performance was evaluated by cross-validated area under the receiver operating characteristic curve (AUC). The SHAP (SHapley Additive exPlanations) method was used to interpret the models and identify key discriminatory features.ResultsSix metabolites—xylose, isocitric acid, fructose, L-fucose, N-acetyl-D-glucosamine (GlcNAc), and glycolic acid—differentiated UC from control group, while three metabolites—xylose, L-fucose, and citric acid—distinguished CD from control group. The optimal diagnostic model achieved a mean AUC of 0.84 for UC and 0.93 for CD. These models retained high diagnostic accuracy even after adjusting for disease activity. SHAP analysis identified L-fucose, xylose, and GlcNAc as important features for UC, and citric acid and xylose for CD.DiscussionOur findings highlight distinct metabolic signatures in central carbon metabolism associated with IBD subtypes. The identified metabolite panels, combined with machine learning models, offer promising non-invasive tools for differentiating UC and CD from healthy individuals.https://www.frontiersin.org/articles/10.3389/fmolb.2025.1615047/fullinflammatory bowel diseaseulcerative colitisCrohn’s diseaseurinary metabolomicsmachine learningcentral carbon metabolism |
| spellingShingle | Miao-Lin Lei Guan-Wei Bi Xiao-Lin Yin Xiao-Lin Yin Yue Wang Yue Wang Yue Wang Zi-Ru Sun Zi-Ru Sun Zi-Ru Sun Xin-rui Guo Hui-peng Zhang Xiao-han Zhao Feng Li Feng Li Yan-Bo Yu Targeted urinary metabolomics combined with machine learning to identify biomarkers related to central carbon metabolism for IBD Frontiers in Molecular Biosciences inflammatory bowel disease ulcerative colitis Crohn’s disease urinary metabolomics machine learning central carbon metabolism |
| title | Targeted urinary metabolomics combined with machine learning to identify biomarkers related to central carbon metabolism for IBD |
| title_full | Targeted urinary metabolomics combined with machine learning to identify biomarkers related to central carbon metabolism for IBD |
| title_fullStr | Targeted urinary metabolomics combined with machine learning to identify biomarkers related to central carbon metabolism for IBD |
| title_full_unstemmed | Targeted urinary metabolomics combined with machine learning to identify biomarkers related to central carbon metabolism for IBD |
| title_short | Targeted urinary metabolomics combined with machine learning to identify biomarkers related to central carbon metabolism for IBD |
| title_sort | targeted urinary metabolomics combined with machine learning to identify biomarkers related to central carbon metabolism for ibd |
| topic | inflammatory bowel disease ulcerative colitis Crohn’s disease urinary metabolomics machine learning central carbon metabolism |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2025.1615047/full |
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