Background and objectives Nonalcoholic fatty liver disease (NAFLD) management is focused on lifestyle modifications, but long‐term maintenance is a challenge for many individuals. This study aimed to evaluate the long‐term effects of two personalized energy‐restricted dietary strategies on weight loss, metabolic and hepatic outcomes in overweight/obese subjects with NAFLD. Methods Ninety‐eight subjects from the Fatty Liver in Obesity (FLiO) study (NCT03183193) were randomly assigned to the American Heart Association (AHA) or the FLiO dietary group in a 2‐year controlled trial. Anthropometry, body composition (DXA), biochemical parameters and hepatic status (ultrasonography, Magnetic Resonance Imaging, and elastography) were assessed at baseline, 6, 12 and 24 months. Results Both the AHA and FLiO diets significantly reduced body weight at 6 (−9.7% vs −10.1%), 12 (−6.7% vs −9.6%), and 24 months (−4.8% vs −7.6%) with significant improvements in body composition, biochemical and liver determinations throughout the intervention. At the end of the follow‐up, the FLiO group showed a greater decrease in ALT, liver stiffness and Fatty Liver Index, among others, compared to AHA group, although these differences were attenuated when the analyses were adjusted by weight loss percentage. The FLiO group also showed a greater increase in adiponectin compared to AHA group. Conclusions The AHA and FLiO diets were able to improve body weight and body composition, as well as metabolic and hepatic status of participants with overweight/obesity and NAFLD within a 2‐year follow‐up. These findings show that both strategies are suitable alternatives for NAFLD management. However, the FLiO strategy may provide more persistent benefits in metabolic and hepatic parameters.

The identification of affordable noninvasive biomarkers for the diagnosis and characterization of nonalcoholic fatty liver disease (NAFLD) is a major challenge for the research community. This study aimed to explore the usefulness of ferritin as a proxy biomarker of NAFLD condition, alone or in combination with other routine biochemical parameters. Subjects with overweight/obesity and ultrasound-confirmed liver steatosis (n = 112) from the Fatty Liver in Obesity (FLiO) study were assessed. The hepatic evaluation considered magnetic resonance imaging, ultrasonography, and credited routine blood liver biomarkers. Anthropometry and body composition, dietary intake (by means of a validated 137-item food frequency questionnaire), and specific biochemical markers were also determined. Serum ferritin levels were analyzed using a chemiluminescent microparticle immunoassay kit. Lower serum ferritin concentrations were associated with general better liver health and nutritional status. The evaluation of ferritin as a surrogate of liver damage by means of quantile regression analyses showed a positive association with alanine aminotransferase (ALT) (β = 19.21; p ≤ 0.001), liver fat content (β = 8.70; p = 0.008), and hepatic iron (β = 3.76; p ≤ 0.001), after adjusting for potential confounders. In receiver operating characteristic (ROC) analyses, the panel combination of blood ferritin, glucose, and ALT showed the best prediction for liver fat mass (area under the curve (AUC) 0.82). A combination of ferritin and ALT showed the higher predictive ability for estimating liver iron content (AUC 0.73). This investigation demonstrated the association of serum ferritin with liver health as well as with glucose and lipid metabolism markers in subjects with NAFLD. Current findings led to the identification of ferritin as a potential noninvasive predictive biomarker of NAFLD, whose surrogate value increased when combined with other routine biochemical measurements (glucose/ALT).

Non-alcoholic fatty liver disease (NAFLD) is a major cause of liver disease worldwide. Some genetic variants might be involved in the progression of this disease. The study hypothesized that individuals with the rs7359397 T allele have a higher risk of developing severe stages of NAFLD compared with non-carriers where dietary intake according to genotypes could have a key role on the pathogenesis of the disease. SH2B1 genetic variant was genotyped in 110 overweight/obese subjects with NAFLD. Imaging techniques, lipidomic analysis and blood liver biomarkers were performed. Body composition, general biochemical and dietary variables were also determined. The SH2B1 risk genotype was associated with higher HOMA-IR p = 0.001; and Fatty Liver Index (FLI) p = 0.032. Higher protein consumption (p = 0.028), less mono-unsaturated fatty acid and fiber intake (p = 0.045 and p = 0.049, respectively), was also referred to in risk allele genotype. Lipidomic analysis showed that T allele carriers presented a higher frequency of non-alcoholic steatohepatitis (NASH) (69.1% vs. 44.4%; p = 0.006). In the genotype risk group, adjusted logistic regression models indicated a higher risk of developing an advanced stage of NAFLD measured by FLI (OR 2.91) and ultrasonography (OR 4.15). Multinomial logistic regression models showed that risk allele carriers had higher liver fat accumulation risk (RRR 3.93) and an increased risk of NASH (RRR 7.88). Consequently, subjects carrying the T allele were associated with a higher risk of developing a severe stage of NAFLD. These results support the importance of considering genetic predisposition in combination with a healthy dietary pattern in the personalized evaluation and management of NAFLD.

Several features of the modern lifestyle, such as weekly schedules or irregular daily eating patterns, have become major drivers of global health problems, including non-alcoholic fatty liver disease (NAFLD). Sleep is an essential component of human well-being, and it has been observed that when circadian rhythms are disrupted, or when sleep quality decreases, an individual’s overall health may worsen. In addition, the discrepancy between the circadian and social clock, due to weekly work/study schedules, is called social jetlag and has also been associated with adverse metabolic profiles. Current management of NAFLD is based on dietary intake and physical activity, with circadian preferences and other environmental factors also needing to be taken into account. In this regard, dietary approaches based on chrononutrition, such as intermittent fasting or time-restricted feeding, have proven to be useful in realigning lifestyle behaviors with circadian biological rhythms. However, more studies are needed to apply these dietary strategies in the treatment of these patients. In this review, we focus on the impact of circadian rhythms and the role of sleep patterns on the pathogenesis and development of NAFLD, as well as the consideration of chrononutrition for the precision nutrition management of patients with NAFLD.

Non-alcoholic fatty liver disease (NAFLD) affects 25% of the global population. The pathogenesis of NAFLD is complex; available data reveal that genetics and ascribed interactions with environmental factors may play an important role in the development of this morbid condition. The purpose of this investigation was to assess genetic and non-genetic determinants putatively involved in the onset and progression of NAFLD after a 6-month weight loss nutritional treatment. A group of 86 overweight/obese subjects with NAFLD from the Fatty Liver in Obesity (FLiO) study were enrolled and metabolically evaluated at baseline and after 6 months. A pre-designed panel of 95 genetic variants related to obesity and weight loss was applied and analyzed. Three genetic risk scores (GRS) concerning the improvement on hepatic health evaluated by minimally invasive methods such as the fatty liver index (FLI) (GRSFLI), lipidomic-OWLiver®-test (GRSOWL) and magnetic resonance imaging (MRI) (GRSMRI), were derived by adding the risk alleles genotypes. Body composition, liver injury-related markers and dietary intake were also monitored. Overall, 23 SNPs were independently associated with the change in FLI, 16 SNPs with OWLiver®-test and 8 SNPs with MRI, which were specific for every diagnosis tool. After adjusting for gender, age and other related predictors (insulin resistance, inflammatory biomarkers and dietary intake at baseline) the calculated GRSFLI, GRSOWL and GRSMRI were major contributors of the improvement in hepatic status. Thus, fitted linear regression models showed a variance of 53% (adj. R2 = 0.53) in hepatic functionality (FLI), 16% (adj. R2 = 0.16) in lipidomic metabolism (OWLiver®-test) and 34% (adj. R2 = 0.34) in liver fat content (MRI). These results demonstrate that three different genetic scores can be useful for the personalized management of NAFLD, whose treatment must rely on specific dietary recommendations guided by the measurement of specific genetic biomarkers.