Selected article for: "liver disease and low sample size"

Author: Wu, Jing; Meng, Qing-Hua
Title: Current understanding of the metabolism of micronutrients in chronic alcoholic liver disease.
  • Cord-id: j79c2q29
  • Document date: 2020_8_21
  • ID: j79c2q29
    Snippet: 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 del
    Document: 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.

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