Determination of the Protein and Amino Acid Content of Fruit, Vegetables and Starchy Roots for Use in Inherited Metabolic Disorders.
Fiona BoyleGary LynchMichelle L KearnsAdam GreenGemma ParrCaoimhe HowardIna KnerrJane RicePublished in: Nutrients (2024)
Amino acid (AA)-related inherited metabolic disorders (IMDs) and urea cycle disorders (UCDs) require strict dietary management including foods low in protein such as fruits, vegetables and starchy roots. Despite this recommendation, there are limited data on the AA content of many of these foods. The aim of this study is to describe an analysis of the protein and AA content of a range of fruits, vegetables and starchy roots, specifically focusing on amino acids (AAs) relevant to AA-related IMDs such as phenylalanine (Phe), methionine (Met), leucine (Leu), lysine (Lys) and tyrosine (Tyr). AA analysis was performed using high-performance liquid chromatography (HPLC) on 165 food samples. Protein analysis was also carried out using the Dumas method. Foods were classified as either 'Fruits', 'Dried fruits', 'Cruciferous vegetables', 'Legumes', 'Other vegetables' or 'Starchy roots'. 'Dried fruits' and 'Legumes' had the highest median values of protein, while 'Fruits' and 'Cruciferous vegetables' contained the lowest median results. 'Legumes' contained the highest and 'Fruits' had the lowest median values for all five AAs. Variations were seen in AA content for individual foods. The results presented in this study provide useful data on the protein and AA content of fruits, vegetables and starchy roots which can be used in clinical practice. This further expansion of the current literature will help to improve diet quality and metabolic control among individuals with AA-related IMDs and UCDs.
Keyphrases
- amino acid
- health risk
- high performance liquid chromatography
- human health
- protein protein
- clinical practice
- systematic review
- health risk assessment
- binding protein
- mass spectrometry
- simultaneous determination
- tandem mass spectrometry
- machine learning
- risk assessment
- heavy metals
- data analysis
- molecularly imprinted