scholarly journals Three Different Genetic Risk Scores Based on Fatty Liver Index, Magnetic Resonance Imaging and Lipidomic for a Nutrigenetic Personalized Management of NAFLD: The Fatty Liver in Obesity Study

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1083
Author(s):  
Nuria Perez-Diaz-del-Campo ◽  
Jose I. Riezu-Boj ◽  
Bertha Araceli Marin-Alejandre ◽  
J. Ignacio Monreal ◽  
Mariana Elorz ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Weight Loss ◽  
Magnetic Resonance ◽  
Dietary Intake ◽  
Fatty Liver ◽  
Genetic Risk ◽  
Risk Scores ◽  
Resonance Imaging ◽  
Personalized Management ◽  
Fatty Liver Index

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.

scholarly journals Three different genetic risk scores based on Fatty Liver Index, Magnetic Resonance Imaging and lipidomic for a nutrigenetic personalized management of NAFLD: the Fatty Liver in Obesity study

2021 ◽  
Author(s):  
Nuria Pérez-Díaz-del-Campo ◽  
Jose I. Riezu-Boj ◽  
Bertha Araceli Marin-Alejandre ◽  
J. Ignacio Monreal ◽  
Mariana Elorz ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Weight Loss ◽  
Magnetic Resonance ◽  
Dietary Intake ◽  
Fatty Liver ◽  
Genetic Risk ◽  
Risk Scores ◽  
Resonance Imaging ◽  
Personalized Management ◽  
Fatty Liver Index

Abstract Background Non-alcoholic fatty liver disease (NAFLD) affects 25% of the global population. The pathogenesis of NAFLD is complex where available data unveils 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-months weight-loss nutritional treatment. Methods 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. Also, a total of 95 single nucleotide polymorphisms (SNPs) related to obesity and weight loss were analyzed by a targeted next-generation sequencing system. 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. Results Overall, 26 SNPs were independently associated with the change on 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 on 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). Conclusion 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. Trial registration: The FLiO study: Fatty Liver in Obesity study, NCT03183193. Registered 12 June 2017 - Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03183193

scholarly journals Effect of APOE ε4 on multimodal brain connectomic traits: a persistent homology study

2020 ◽  
Vol 21 (S21) ◽  
Author(s):  
Jin Li ◽  
◽  
Chenyuan Bian ◽  
Dandan Chen ◽  
Xianglian Meng ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Genetic Risk ◽  
Brain Connectivity ◽  
Persistent Homology ◽  
Brain Network ◽  
Resonance Imaging ◽  
Modeling Framework ◽  
Apoe Ε4 ◽  
Topological Features

Abstract Background Although genetic risk factors and network-level neuroimaging abnormalities have shown effects on cognitive performance and brain atrophy in Alzheimer’s disease (AD), little is understood about how apolipoprotein E (APOE) ε4 allele, the best-known genetic risk for AD, affect brain connectivity before the onset of symptomatic AD. This study aims to investigate APOE ε4 effects on brain connectivity from the perspective of multimodal connectome. Results Here, we propose a novel multimodal brain network modeling framework and a network quantification method based on persistent homology for identifying APOE ε4-related network differences. Specifically, we employ sparse representation to integrate multimodal brain network information derived from both the resting state functional magnetic resonance imaging (rs-fMRI) data and the diffusion-weighted magnetic resonance imaging (dw-MRI) data. Moreover, persistent homology is proposed to avoid the ad hoc selection of a specific regularization parameter and to capture valuable brain connectivity patterns from the topological perspective. The experimental results demonstrate that our method outperforms the competing methods, and reasonably yields connectomic patterns specific to APOE ε4 carriers and non-carriers. Conclusions We have proposed a multimodal framework that integrates structural and functional connectivity information for constructing a fused brain network with greater discriminative power. Using persistent homology to extract topological features from the fused brain network, our method can effectively identify APOE ε4-related brain connectomic biomarkers.

scholarly journals Utility of magnetic resonance imaging versus histology for quantifying changes in liver fat in nonalcoholic fatty liver disease trials

Hepatology ◽  
2013 ◽  
Vol 58 (6) ◽  
pp. 1930-1940 ◽  
Author(s):  
Mazen Noureddin ◽  
Jessica Lam ◽  
Michael R. Peterson ◽  
Michael Middleton ◽  
Gavin Hamilton ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Liver Disease ◽  
Magnetic Resonance ◽  
Fatty Liver ◽  
Nonalcoholic Fatty Liver Disease ◽  
Fatty Liver Disease ◽  
Liver Fat ◽  
Resonance Imaging ◽  
Nonalcoholic Fatty Liver

Magnetic resonance imaging-based balance analysis of linoleate utilization during weight loss in obese humans

Lipids ◽  
1999 ◽  
Vol 34 (S1) ◽  
pp. S89-S90 ◽  
Author(s):  
S. C. Cunnane ◽  
R. Ross ◽  
J. L. Bannister ◽  
D. J. A. Jenkins
Keyword(s):  
Magnetic Resonance Imaging ◽  
Weight Loss ◽  
Magnetic Resonance ◽  
Resonance Imaging ◽  
Balance Analysis
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