Effect of omega-3 supplementation on cardiometabolic indices in diabetic patients with non-alcoholic fatty liver disease: a randomized controlled trial

Functional Food Chemical Ingredient Strategies for Non-alcoholic Fatty Liver Disease (NAFLD) and Hepatic Fibrosis: Chemical Properties, Health Benefits, Action, and Application

PURPOSE OF REVIEW

The liver is an important digestive gland in the body. Lifestyle and dietary habits are increasingly damaging our liver, leading to various diseases and health problems. Non-alcoholic fatty liver disease (NAFLD) has become one of the most serious liver disease problems in the world. Diet is one of the important factors in maintaining liver health. Functional foods and their components have been identified as novel sources of potential preventive agents in the prevention and treatment of liver disease in daily life. However, the effects of functional components derived from small molecules in food on different types of liver diseases have not been systematically summarized.

RECENT FINDINGS

The components and related mechanisms in functional foods play a significant role in the development and progression of NAFLD and liver fibrosis. A variety of structural components are found to treat and prevent NAFLD and liver fibrosis through different mechanisms, including flavonoids, alkaloids, polyphenols, polysaccharides, unsaturated fatty acids, and peptides. On the other hand, the relevant mechanisms include oxidative stress, inflammation, and immune regulation, and a large number of literature studies have confirmed a close relationship between the mechanisms. The purpose of this article is to examine the current literature related to functional foods and functional components used for the treatment and protection against NAFLD and hepatic fibrosis, focusing on chemical properties, health benefits, mechanisms of action, and application in vitro and in vivo. The roles of different components in the biological processes of NAFLD and liver fibrosis were also discussed.

Development of a novel (poly)phenol-rich diet score and its association with urinary (poly)phenol metabolites

Background: Estimating (poly)phenol intake is challenging due to inadequate dietary assessment tools and limited food content data. Currently, a priori diet scores to characterise (poly)phenol-rich diets are lacking. This study aimed to develop a novel (poly)phenol-rich diet score (PPS) and explore its relationship with circulating (poly)phenol metabolites. Methods: A total of 543 healthy free-living participants aged 18-80 years completed a food frequency questionnaire (FFQ) (EPIC-Norfolk) and provided 24 h urine samples. The PPS was developed based on the relative intake (quintiles) of 20 selected (poly)phenol-rich food items abundant in the UK diet, including tea, coffee, red wine, whole grains, chocolate and cocoa products, berries, apples and juice, pears, grapes, plums, citrus fruits and juice, potatoes and carrots, onions, peppers, garlic, green vegetables, pulses, soy and soy products, nuts, and olive oil. Foods included in the PPS were chosen based on their (poly)phenol content, main sources of (poly)phenols, and consumption frequencies in the UK population. Associations between the PPS and urinary phenolic metabolites were investigated using linear models adjusting energy intake and multiple testing (FDR adjusted p < 0.05). Result: The total PPS ranged from 25 to 88, with a mean score of 54. A total of 51 individual urinary metabolites were significantly associated with the PPS, including 39 phenolic acids, 5 flavonoids, 3 lignans, 2 resveratrol and 2 other (poly)phenol metabolites. The total (poly)phenol intake derived from FFQs also showed a positive association with PPS (stdBeta 0.32, 95% CI (0.24, 0.40), p < 0.01). Significant positive associations were observed in 24 of 27 classes and subclasses of estimated (poly)phenol intake and PPS, with stdBeta values ranging from 0.12 (0.04, 0.20) for theaflavins/thearubigins to 0.43 (0.34, 0.51) for flavonols (p < 0.01). Conclusion: High adherence to the PPS diet is associated with (poly)phenol intake and urinary biomarkers, indicating the utility of the PPS to characterise diets rich in (poly)phenols at a population level.

Atherogenic Index of Plasma in Non-Alcoholic Fatty Liver Disease: Systematic Review and Meta-Analysis

(1) Background: Approximately a billion people worldwide are affected by NAFLD, which places a high clinical burden and financial cost on society. Liver biopsy is the gold standard for diagnosing NAFLD, but its invasivity limits the early diagnosis of NAFLD. Hence, it is important to look for alternate techniques in detecting and diagnosing NAFLD. NAFLD is associated with atherosclerosis. The purpose of this study was to assess the effectiveness of the atherogenic index of plasma (AIP) as a non-invasive modality for predicting NAFLD. (2) Methods: A search using electronic databases PubMed, EMBASE, and Scopus was carried out to find observational studies, looking at research that had been published up until the date of 11 May 2022. The included studies’ quality, risk of bias, and internal validity were evaluated using the QUADAS-2 quality assessment tool. The key summary outcomes were the mean difference (MD) and area under the curve (AUC). (3) Results: A total of eight studies (81,178 participants) were included in our review, while 17% of the included participants had NAFLD. A sex distribution of 57.8% men and 42.2% women was observed. The AIP between NAFLD and the controls was not significant (MD 0.212 [95% CI 0.231–0.655]). A significant MD in AIP between the males and females with NAFLD was observed (MD 0.246 [95% CI 0.098–0.395]). The AIP predicted NAFLD with an AUC of 0.764 as well as in males (AUC 0.761) and females (AUC 0.733). (4) Conclusions: There was a substantial MD in the AIP between both sexes, but there was no significant difference in the AIP values between patients with NAFLD and the controls. The AIP is a reliable biomarker for the diagnosis of NAFLD since its ability to predict the development of NAFLD was comparable to that of the other biomarkers.