Non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) have been linked to changes in amino acid (AA) levels. The objective of the current study was to examine the relationship between MRI parameters that reflect inflammation and fibrosis and plasma AA concentrations in NAFLD patients. Plasma AA levels of 97 NAFLD patients from the MAST4HEALTH study were quantified with liquid chromatography. Medical, anthropometric and lifestyle characteristics were collected and biochemical parameters, as well as inflammatory and oxidative stress biomarkers, were measured. In total, subjects with a higher MRI-proton density fat fraction (MRI-PDFF) exhibited higher plasma AA levels compared to subjects with lower PDFF. The concentrations of BCAAs (p-Value: 0.03), AAAs (p-Value: 0.039), L-valine (p-Value: 0.029), L-tyrosine (p-Value: 0.039) and L-isoleucine (p-Value: 0.032) were found to be significantly higher in the higher PDFF group compared to lower group. Plasma AA levels varied according to MRI-PDFF. Significant associations were also demonstrated between AAs and MRI-PDFF and MRI-cT1, showing the potential utility of circulating AAs as diagnostic markers of NAFLD.

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is the leading cause of liver-related morbidity and mortality. Although the invasive liver biopsy remains the golden standard for MASLD diagnosis, Magnetic Resonance Imaging-derived Proton Density Fat Fraction (MRI-PDFF) is an accurate, non-invasive method for the assessment of treatment response. This study aimed at developing a Polygenic Risk Score (PRS) to improve MRI-PDFF prediction using UK Biobank data to assess an individual’s genetic liability to MASLD. Methods: We iteratively sequestered 10% of MRI-PDFF samples as a validation set and split the rest of each dataset into base and target partitions, containing GWAS summary statistics and raw genotype data, respectively. PRSice2 was deployed to derive PRS candidates. Based on the frequency of SNP appearances along the PRS candidates, we generated different SNP sets according to variable frequency cutoffs. By applying the PRSs to the validation set, we identified the optimal SNP set, which was then applied to a Greek nonalcoholic fatty liver disease (NAFLD) study. Results: Data from 3553 UK Biobank participants yielded 49 different SNP sets. After calculating the PRS on the validation set for every SNP set, an optimal PRS with 75 SNPs was selected (incremental R² = 0.025, p-value = 0.00145). Interestingly, 43 SNPs were successfully mapped to MASLD-related known genes. The selected PRS could predict traits, like LDL cholesterol and diastolic blood pressure in the UK Biobank, as also disease outcome in the Greek NAFLD study. Conclusions: Our findings provide strong evidence that PRS is a powerful prediction model for MASLD, while it can also be applied on populations of different ethnicity.

An ensemble of confounding factors, such as an unhealthy diet, obesity, physical inactivity, and smoking, have been linked to a lifestyle that increases one’s susceptibility to chronic diseases and early mortality. The circulatory metabolome may provide a rational means of pinpointing the advent of metabolite variations that reflect an adherence to a lifestyle and are associated with the occurrence of chronic diseases. Data related to four major modifiable lifestyle factors, including adherence to the Mediterranean diet (estimated on MedDietScore), body mass index (BMI), smoking, and physical activity level (PAL), were used to create the lifestyle risk score (LS). The LS was further categorized into four groups, where a higher score group indicates a less healthy lifestyle. Drawing on this, we analyzed 223 NMR serum spectra, 89 MASLD patients and 134 controls; these were coupled to chemometrics to identify “key” features and understand the biological processes involved in specific lifestyles. The unsupervised analysis verified that lifestyle was the factor influencing the samples’ differentiation, while the supervised analysis highlighted metabolic signatures. Τhe metabolic ratios of alanine/formic acid and leucine/formic acid, with AUROC > 0.8, may constitute discriminant indexes of lifestyle. On these grounds, this research contributed to understanding the impact of lifestyle on the circulatory metabolome and highlighted “prudent lifestyle” biomarkers.

Whereas the etiology of non-alcoholic fatty liver disease (NAFLD) is complex, the role of nutrition as a causing and preventive factor is not fully explored. The aim of this study is to associate dietary patterns with magnetic resonance imaging (MRI) parameters in a European population (Greece, Italy, and Serbia) affected by NAFLD. For the first time, iron-corrected T1 (cT1), proton density fat fraction (PDFF), and the liver inflammation fibrosis score (LIF) were examined in relation to diet. A total of 97 obese patients with NAFLD from the MAST4HEALTH study were included in the analysis. A validated semi-quantitative food frequency questionnaire (FFQ) was used to assess the quality of diet and food combinations. Other variables investigated include anthropometric measurements, total type 2 diabetes risk, physical activity level (PAL), and smoking status. Principal component analysis (PCA) was performed to identify dietary patterns. Six dietary patterns were identified, namely “High-Sugar”, “Prudent”, “Western”, “High-Fat and Salt”, “Plant-Based”, and “Low-Fat Dairy and Poultry”. The “Western” pattern was positively associated with cT1 in the unadjusted model (beta: 0.020, p-value: 0.025) and even after adjusting for age, sex, body mass index (BMI), PAL, smoking, the center of the study, and the other five dietary patterns (beta: 0.024, p-value: 0.020). On the contrary, compared with low-intake patients, those with medium intake of the “Low-Fat Dairy and Poultry” pattern were associated with lower values of cT1, PDFF, and LIF. However, patients with a “Low-Fat Dairy and Poultry” dietary pattern were negatively associated with MRI parameters (cT1: beta: −0.052, p-value: 0.046, PDFF: beta: −0.448, p-value: 0.030, LIF: beta: −0.408, p-value: 0.025). Our findings indicate several associations between MRI parameters and dietary patterns in NAFLD patients, highlighting the importance of diet in NAFLD.