Review
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Aug 21, 2020; 26(31): 4567-4578
Published online Aug 21, 2020. doi: 10.3748/wjg.v26.i31.4567
Current understanding of the metabolism of micronutrients in chronic alcoholic liver disease
Jing Wu, Qing-Hua Meng
Jing Wu, Qing-Hua Meng, Department of Critical Care Medicine of Liver Disease, Beijing You-An Hospital, Capital Medical University, Beijing 100069, China
Author contributions: Wu J and Meng QH conceived and outlined the review; Wu J performed the literature review and wrote the manuscript; Wu J made critical revisions. All authors approved the final version.
Supported by the Municipal Natural Science Foundation of Beijing, China, No. 7192085.
Conflict-of-interest statement: The authors declare no conflict of interests.
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: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Qing-Hua Meng, MD, PhD, Chief Doctor, Professor, Department of Critical Care Medicine of Liver Disease, Beijing You-An Hospital, Capital Medical University, No. 8 Xitoutiao, Youanmen Wai, Fengtai District, Beijing 100069, China. meng_qh@126.com
Received: June 4, 2020
Peer-review started: June 4, 2020
First decision: June 18, 2020
Revised: June 22, 2020
Accepted: July 30, 2020
Article in press: July 30, 2020
Published online: August 21, 2020
Processing time: 77 Days and 16.8 Hours
Abstract

Alcoholic liver disease (ALD) remains an important health problem worldwide. Perturbation of micronutrients has been broadly reported to be a common characteristic in patients with ALD, given the fact that micronutrients often act as composition or coenzymes of many biochemical enzymes responsible for the inflammatory response, oxidative stress, and cell proliferation. Mapping the metabolic pattern and the function of these micronutrients is a prerequisite before targeted intervention can be delivered in clinical practice. Recent years have registered a significant improvement in our understanding of the role of micronutrients on the pathogenesis and progression of ALD. However, how and to what extent these micronutrients are involved in the pathophysiology of ALD remains largely unknown. In the current study, we provide a review of recent studies that investigated the imbalance of micronutrients in patients with ALD with a focus on zinc, iron, copper, magnesium, selenium, vitamin D and vitamin E, and determine how disturbances in micronutrients relates to the pathophysiology of ALD. Overall, zinc, selenium, vitamin D, and vitamin E uniformly exhibited a deficiency, and iron demonstrated an elevated trend. While for copper, both an elevation and deficiency were observed from existing literature. More importantly, we also highlight several challenges in terms of low sample size, study design discrepancies, sample heterogeneity across studies, and the use of machine learning approaches.

Keywords: Alcoholic liver disease; Metabolism; Trace elements; Vitamins; Malnutrition; Oxidative stress

Core tip: Perturbation of micronutrients has been broadly reported to be a common characteristic in patients with alcoholic liver disease (ALD). In the current study, we provide a review of recent studies that investigated the imbalance of micronutrients in patients with ALD with a focus on zinc, iron, copper, magnesium, selenium, vitamin D and vitamin E, and determine how disturbances in micronutrients relates to the pathophysiology of ALD. More importantly, we also highlight several challenges in terms of low sample size, study design discrepancies, sample heterogeneity across studies, and the use of machine learning approaches.