JOURNAL ARTICLE
HIGH-DEMAND AMINO ACIDS COULD PLAY AN IMPORTANT ROLE IN SUPPORTING RECOVERY IN MEDICAL PRACTICE.
Published In: Australasian College of Nutritional & Environmental Medicine Journal, 2023, v. 42, n. 4. P. 68 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Dunstan, Richard Hugh 3 of 3
Abstract
High-Demand Amino Acids (HDAA) play a pivotal role in human metabolism, comprising histidine, serine, glycine, lysine, ornithine and aspartic acid. These amino acids are in high demand in the body because they are utilised in multiple biochemical pathways as well as protein synthesis. Notably, glycine holds a significant place, constituting one-third of collagen proteins. Despite their importance, HDAA are lost rapidly through sweat and urine, intensifying during daily activities, trauma, infections, and exercise. There are no stores of amino acids in the body. Their supply for metabolism is achieved by the catabolism of muscle proteins to supply the HDAA as required by the body. HDAA supplied via exogenous supplementation can be rapidly absorbed without the need for digestion and can alleviate the demand for muscle catabolism. This, in turn, can help minimise loss of muscle mass and thereby support recovery from illness as well as exercise. The proposal to replenish HDAA at levels equivalent to daily deficits emerges as a promising strategy, offering a targeted approach to supplementation in the pursuit of enhanced health and recovery. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Australasian College of Nutritional & Environmental Medicine Journal. 2023/12, Vol. 42, Issue 4, p68
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2023
- ISSN:1328-8040
- Accession Number:177689159
- Copyright Statement:Copyright of Australasian College of Nutritional & Environmental Medicine Journal is the property of Copyright Agency Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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