NALFD has reached pandemic levels being recognized as an important health burden with an urgent need for early diagnosis . Genetic predisposition for NAFLD has been reported [17, 19]. With this in mind, the objective of this research was to assess the impact of the interaction between genetic and non-genetic factors concerning the improvement of the hepatic health using different diagnosis tools (FLI, MRI and OWLiver®-test) after a 6-month energy-restricted nutritional treatment for a more personalize management of this liver disease.
The high prevalence of NAFLD could be related to its strong link with obesity, which seems to play a role in both the initial simple steatosis and in its progression to NASH [11, 56]. In this context, various genes and less frequent variants have been associated with the regulation of energy metabolism [17, 57]. Moreover, the increasing of knowledge of the genetic component of NAFLD has promoted the development of noninvasive diagnosis methods based on Genome Wide Associations Studies (GWAs) [11, 15, 58–60], but few of them have examined the contribution of obesity-related variants linked to the evolution of this hepatic disease [35, 61].
In order to better understand the contribution of genetics in the context of NAFLD three different GRS were constructed based on the improvement of hepatic health after an energy-restricted treatment measured by three non-invasive diagnostic methods (FLI, Magnetic Resonance Imaging and OWLiver®-test). On the one hand, Fatty Liver Index has been highly correlated with measures of fatty liver disease showing an area under the curve of 0.84, predicting most cases of NAFLD [62, 63]. Moreover, a recent study reported that the FLI joint to the waist circumference-to-height ratio could be one of the most accurate algorithms for the noninvasive diagnosis of NAFLD in both lean and overweight/obese population [64, 65]. On the other hand, MRI can be considered the gold standard for steatosis measurement being highly accurate and reproducible and superior in detecting and quantifying fat accumulation [61, 66]. However, these two methods have limitations in detecting inflammation, ballooning and cellular injury, which are key components in NASH diagnosis . Thus, in some cases, models base on “omics” sciences such as the OWLiver®-test, could be of interest adding knowledge about diverse factors influencing weight-loss variability among individuals. Due to the differences between methods in outcome measures, distinct genes and so pathways may be expected to be connected.
Therefore, a total of 38 polymorphisms were independently associated with differential responses to hepatic functionality (FLI), fat liver content (MRI) and lipid metabolism (OWLiver®-test). It is important to emphasize that each non-invasive diagnostic method has its specific SNPs. Only the rs2959272 (PPARG) genetic variant was the common element on the three GRS. In this sense, an intervention study indicated that the PPARG genotype was associated with success in body weight reduction . Indeed, two common elements were also observed between GRSFLI and GRSMRI, SNPs were located in genes related to bile secretion (ABCB11) and the regulation of energy balance and body weight (SH2B1). Meanwhile, SNPs in genes implicated in weight loss (SH2B1 and STK33) influenced both GRSOWL and GRSMRI. Instead, 3 common elements mapped to genes involved in endocrine/enzymatic regulation of lipid metabolism affecting macronutrient (GNAS) food intake and energy expenditure (MC4R) and thermogenesis (UCP1) were observed in GRSFLI and GRSOWL, clarify the detailed path-way between body weight changes and the functions of the PPARG gene and SNPs
In this study, a greater change in most of the NAFLD-related variables was reported when the genetic risk was lower. According to these findings, it has been extensively debated the identification of the physiological pathways that control energy metabolism and body weight regulation [29, 69, 70]. A Genetic Investigation of ANtropometric Traits consortium (GIANT) metanalysis identified 97 loci for BMI where genes near these specific loci showed expression enrichment in the central nervous system, suggesting that BMI is mainly regulated by processes such as hypothalamic control of energy intake . Similar results have been found in a recent study in a pediatric population, where the application of a GRS to established clinical risk factors significantly improved the discriminatory capability of the prediction of NAFLD risk . Indeed, different genetic variants and interactions with environmental factors have been shown to modulate the differential individual responses to moderately high-protein and low-fat dietary interventions in a Caucasian population . In this sense, genetic information could help to determine the most appropriate dietary intervention for the prevention and treatment of NAFLD, as well as the development of associated comorbidities [72, 73]
Moreover, for the purpose of explaining the variability on the improvement in hepatic functionality (FLI), liver fat content (by MRI) and lipidomic (OWLiver®-test), linear regression models were performed. The predictive accuracy of all models substantially improved when combining each of the previously mentioned SNPs in the multiple linear regression models, which is in line with previous studies. In order to improve these results, each regression model was fitted by sex, age and NAFLD related variables such as inflammatory biomarkers or dietary compounds. Factors related with proinflammatory and profibrogenetic pathways such as leptin, adiponectin, or FGF-21, which appears to be elevated in patients with NAFLD, are therefore a promising target for the treatment [75, 76]. Thus, GRSFLI, GRSMRI and GRSOWL were major predictor of the change in FLI, liver fat content (MRI) and OWLiver®-test, respectively.
To the best of our knowledge, there are few studies showing the combined effects of GRS built from SNPs related weight and adiposity regulation in response to different energy-restricted diets [48, 77]. Moreover, it has been reported that the genetic background is an important factor explaining metabolically health and unhealthy phenotypes related to obesity, in addition to lifestyle variables.
In this sense, dietary factors seem to be of key importance and have been associated with weight gain, obesity and NAFLD development [78, 79]. Interestingly, in this research, higher baseline protein was associated with worst hepatic health improvement measured by FLI and OWLiver®-test. Furthermore, an interaction between the liver fat content assessed by MRI and baseline protein was found. In the same line were the results obtained from the Nurse´s Health Study and the Health Professionals Follow-up Study where an increased intake of sugars-sweetened beverages was found to amplify the association of a 32-SNP genetic risk score with BMI . These findings suggest that not only genetic and dietary factor should be considered but also the interaction of both of them [81–83]. Hence, a combined analysis over 16,000 children and adolescent showed the FTO rs9939609 variant that confers a predisposition to higher BMI is associated with higher total energy intake and that lower dietary protein intake attenuates the association .Among the macronutrient categories, protein is the main one that contributes to the satiety, contributing therefore to weight-loss [85, 86]. However, the effect of the high protein diet in patients with NAFLD remain controversial. On the one hand, it has been suggested that the consumption of specific dietary amino acids might negatively impact on liver status and, to a lesser extent on glucose metabolism in subjects with overweight/obesity and NAFLD . Moreover, high protein intake derived mainly from dairy products has been associated with higher risk to develop diabetes and also with NAFLD . A recent study has also suggested that following a lower protein diet, particularly in genetically predisposed individuals, might be an effective approach for addressing cardiometabolic diseases among Southeast Asian women . On the other hand, high protein diet has been reported to be a valid therapeutic approach to revert NAFLD, being of special importance the protein source and the functional status of the liver . Besides, BCAA supplementation has been demonstrated to ameliorate liver fibrosis and suppress tumor growth in a rat model of HCC with liver cirrhosis, as well as alleviate hepatic steatosis and liver injury in NASH mice [91, 92].
As for drawbacks of this research: Firstly, liver biopsy results were not available to corroborate the precise diagnosis of patients . Nonetheless, in this research we carried out a complete evaluation of the liver status by means of validated and widely used techniques as well as blood biomarkers and hepatic indexes, which are affordable and practical methods to use in public health settings. Second, the sample size and the enrollment of subjects are not very large. For this reason, these models should be further validated in different populations, to establish whether it indeed represents a reliable and accurate, “non-invasive alternative” to liver biopsy, as well as the role of new SNPs associated with excessive adiposity and accompanying metabolic alterations through a GRS approach needs to be explored. Thirdly, type I and type II errors cannot be completely ruled out, especially those related to the selection of SNPs to be introduced into the GRS. However, due to the use of less stringent P value thresholds compared to association studies of single variants, genomic profile risk scoring analyses can tolerate, at balance, some of these biases, as previously reviewed . Fourthly, dietary intake was evaluated using self-reported information of the participants, which may produce some bias on the evaluation of the results. Lastly the constructions of the GRS using specific obesity-related SNP it is also an important limitation. However, the inclusion of these SNP on the evaluation of the genetic influence on NAFLD could be also considered an important strength of this investigation, as well as the use of different multiple linear regression models to test the contribution of genetic, baseline protein and inflammatory factors on the management of NAFLD. Finally, the study is a randomized controlled trial where each volunteer has had an individual follow-up promoting the adoption of behavioral changes and a healthy lifestyle.
Overall, this experiment was designed as a proof of concept in order to evaluate if the genetic background linked to NAFLD-related factors may influence hepatic amelioration. In addition, examining new causes of disease and the underlying mechanism or alteration in specific pathways and clinical outcomes may be of interest.