Martínez JA, Alonso-Bernáldez M, Martínez-Urbistondo D, Vargas-Nuñez JA, Ramírez de Molina A, Dávalos A, Ramos-Lopez O. Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases. World J Gastroenterol 2022; 28(44): 6230-6248 [PMID: 36504554 DOI: 10.3748/wjg.v28.i44.6230]
Corresponding Author of This Article
Omar Ramos-Lopez, PhD, Professor, Medicine and Psychology School, Autonomous University of Baja California, Universidad 14418, UABC, Parque Internacional Industrial, Tijuana 22390, Baja California, Mexico. oscar.omar.ramos.lopez@uabc.edu.mx
Research Domain of This Article
Endocrinology & Metabolism
Article-Type of This Article
Review
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastroenterol. Nov 28, 2022; 28(44): 6230-6248 Published online Nov 28, 2022. doi: 10.3748/wjg.v28.i44.6230
Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases
J Alfredo Martínez, Marta Alonso-Bernáldez, Diego Martínez-Urbistondo, Juan A Vargas-Nuñez, Ana Ramírez de Molina, Alberto Dávalos, Omar Ramos-Lopez
J Alfredo Martínez, Marta Alonso-Bernáldez, Precision Nutrition and Cardiometabolic Health, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
Diego Martínez-Urbistondo, Department of Internal Medicine, Hospital Universitario HM Sanchinarro, Madrid 28050, Spain
Juan A Vargas-Nuñez, Servicio de Medicina Interna, Hospital Universitario Puerta de Hierro Majadahonda, Madrid 28222, Majadahonda, Spain
Ana Ramírez de Molina, Molecular Oncology and Nutritional Genomics of Cancer, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
Alberto Dávalos, Laboratory of Epigenetics of Lipid Metabolism, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
Omar Ramos-Lopez, Medicine and Psychology School, Autonomous University of Baja California, Tijuana 22390, Baja California, Mexico
Author contributions: Martínez JA and Alonso-Bernáldez M contributed equally to this work as first co-authors. Martínez JA and Ramos-Lopez O conceived and designed the study; Martínez JA, Alonso-Bernáldez M, and Ramos-Lopez O performed the search of articles and wrote the draft of the manuscript; Martínez-Urbistondo D, Vargas-Nuñez JA, Dávalos A, and Ramos-Lopez O contributed to the analysis and critical interpretation of the data; and all authors read and approved the final manuscript.
Supported bythe Community of Madrid and the European Union, through the European Regional Development Fund (ERDF)-REACT-EU resources of the Madrid Operational Program 2014–2020, in the action line of R + D + i projects in response to COVID-19, FACINGLCOVID-CM”; Synergic R&D Projects in New and Emerging Scientific Areas on the Frontier of Science and Interdisciplinary Nature of The Community of Madrid, METAINFLAMATION-Y2020/BIO-6600.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Omar Ramos-Lopez, PhD, Professor, Medicine and Psychology School, Autonomous University of Baja California, Universidad 14418, UABC, Parque Internacional Industrial, Tijuana 22390, Baja California, Mexico. oscar.omar.ramos.lopez@uabc.edu.mx
Received: September 11, 2022 Peer-review started: September 11, 2022 First decision: September 29, 2022 Revised: October 7, 2022 Accepted: November 16, 2022 Article in press: November 16, 2022 Published online: November 28, 2022 Processing time: 74 Days and 5.9 Hours
Abstract
The liver is a key organ involved in a wide range of functions, whose damage can lead to chronic liver disease (CLD). CLD accounts for more than two million deaths worldwide, becoming a social and economic burden for most countries. Among the different factors that can cause CLD, alcohol abuse, viruses, drug treatments, and unhealthy dietary patterns top the list. These conditions prompt and perpetuate an inflammatory environment and oxidative stress imbalance that favor the development of hepatic fibrogenesis. High stages of fibrosis can eventually lead to cirrhosis or hepatocellular carcinoma (HCC). Despite the advances achieved in this field, new approaches are needed for the prevention, diagnosis, treatment, and prognosis of CLD. In this context, the scientific com-munity is using machine learning (ML) algorithms to integrate and process vast amounts of data with unprecedented performance. ML techniques allow the integration of anthropometric, genetic, clinical, biochemical, dietary, lifestyle and omics data, giving new insights to tackle CLD and bringing personalized medicine a step closer. This review summarizes the investigations where ML techniques have been applied to study new approaches that could be used in inflammatory-related, hepatitis viruses-induced, and coronavirus disease 2019-induced liver damage and enlighten the factors involved in CLD development.
Core Tip: Chronic liver disease has become a global burden, and new approaches need to be explored to tackle this disease. In this context, machine learning techniques bring a whole new set of opportunities to study novel approaches and biomarkers for prevention, diagnosis, treatment, and prognosis of inflammatory and virus-related liver diseases. The application of machine learning algorithms constitutes a pivotal piece of personalized medicine, allowing the integration of different phenotypical and genotypical data for a precision outcome concerning inflammatory liver comorbidities in non-communicable and viral diseases.
