Whole Metagenomic Profiling Identifies a Gut Microbial Signature for Chronic Pancreatitis via Machine Learning.
Обработка статьи · этапы, ошибки и управление
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Bartelli Thais F, Baydogan Seyda, Sahin Ismet, Hoffman Kristi L, Petrosino Joseph, Blackburn Kyle W, Zhao Jing, Wood Amy, Ayvaz Tulin, Surathu Anil, Cagigas Martha Navarro, Barcenas Erick Carrasco, Mata Tomera, Nguyen Vincent Kim, Zulbaran-Rojas Alejandro, Li Le, Faraoni Erika Y, White James R, Ajami Nadim, Li Liang, Yadav Dhiraj, Conwell Darwin L, Serrano Jose, Pandol Stephen J, Fogel Evan L, Van Den Eden Stephen K, Vege Santhi Swaroop, Topazian Mark D, Park Walter G, Hart Phil A, Forsmark Chris, Bellin Melena D, Maitra Anirban, Bhutani Manoop S, Kim Michael, Van Buren George, Fisher William E, McAllister Florencia, on Behalf
Аннотация
OBJECTIVE: To determine whether the oral or gut microbial signature can classify chronic pancreatitis (CP).
METHODS: Stool samples (n=707) were collected from participants in the Prospective Evaluation of Chronic Pancreatitis for Epidemiologic and Translational Studies (PROCEED). Samples were distributed among 200 healthy (HC), 310 CP, 49 acute pancreatitis (AP), and 148 recurrent acute pancreatitis (RAP). In addition, saliva samples were collected for a subset of participants (n=156). Whole genome sequencing was performed to assess microbiome composition. Machine learning algorithms were utilized to identify a signature with microbial features predictive of CP.
RESULTS: Gut alpha diversity was significantly decreased in AP, RAP, and CP compared with HC, with CP exhibiting the lowest diversity. In contrast, oral microbial diversity showed no significant variation across groups. Beta diversity analysis revealed distinct gut microbiome compositions between HC and pancreatitis subtypes, with CP showing the most pronounced differences. Random forest models using gut microbial species demonstrated robust predictive performance for CP using a minimum of 10 species (Area under the curve-AUC: 0.834; accuracy: 0.774). Despite similarities in gut microbiome composition across pancreatitis subtypes, a unique gut microbial signature for CP was identified highlighting the microbiome's potential in CP diagnosis.
CONCLUSION: Our study reveals a gut microbial signature predictive of CP using machine learning models in a large US multi-institutional cohort.