Nutrition in Gilbert's Syndrome-A Systematic Review of Clinical Trials According to the PRISMA Statement.
Zuzanna Sabina GoluchAldona Wierzbicka-RucińskaEwelina Ewa KsiążekPublished in: Nutrients (2024)
Gilbert syndrome is the most common hyperbilirubinemia, associated with a mutation in the UGT1A1 bilirubin gene, which produces an enzyme that conjugates bilirubin with glucuronic acid. Episodes of jaundice occurring in GS negatively affect patients' quality of life. This systematic review aimed to analyze clinical studies regarding nutrition in people with GS. The study followed the PRISMA guidelines and utilized the Ebsco, Embase, Cochrane, PubMed, Scopus, and Web of Science databases to search clinical trials focused on diet/nutrition in GS (1963-2023 years). The methodological quality of selected studies was assessed using the Jadad scale. As a result, 19 studies met the inclusion criteria. The research mainly focused on the impact of caloric restriction, consumption of various diet variants, and vegetables and fruits on hyperbilirubinemia and metabolic health. A nutritional intervention consisting of not applying excessive calorie restrictions and consuming fats and biologically active compounds in vegetables and fruits ( Cruciferae , Apiaceous , Rutaceae ) may prevent the occurrence of jaundice episodes. It is justified to conduct further research on detecting such compounds in food, which, by influencing the expression of the UGT liver enzyme gene, could contribute to regulating bilirubin concentration in the blood of people with GS.
Keyphrases
- physical activity
- clinical trial
- systematic review
- meta analyses
- copy number
- weight loss
- public health
- end stage renal disease
- human health
- randomized controlled trial
- ejection fraction
- risk assessment
- newly diagnosed
- healthcare
- chronic kidney disease
- genome wide
- poor prognosis
- mental health
- case control
- case report
- health risk
- genome wide identification
- gene expression
- phase ii
- body mass index
- weight gain
- tyrosine kinase
- dna methylation
- machine learning
- patient reported outcomes
- artificial intelligence
- heavy metals
- health risk assessment
- phase iii
- open label
- quality improvement
- deep learning
- long non coding rna
- drug delivery