Omics-based Predictors of NAFLD/Potential NASH

Omics-based Predictors of NAFLD/Potential NASH: A New Era Towards Valid and Reliable Non-invasive Diagnosis and Personalized Therapy

The cascade of care for the non-alcoholic fatty liver disease (NAFLD) and its progression to non-alcoholic steatohepatitis (NASH) requires crossing the barriers for their diagnosis and treatment. The multifactorial nature of NAFLD/NASH limits their diagnosis by a single factor solely. This project aimed at developing a powerful composite marker panel based on multi-omics technologies to detect NAFLD without or with fibrosis (potential for NASH) in high-risk populations (obesity, type 2 diabetes, hypertensive, dyslipidemia). This project is an exploratory study to unrevealing the intra-heterogeneity and inter-similarities of NAFLD without and with fibrosis versus those of healthy individuals. The molecular and clinical characteristics of 450 participants (225 adults aged 30-60 years and 225 children aged 12 -18 years) will be investigated; 150 NAFLD patients without, 150 NAFLD patients with fibrosis (potential NASH) compared to 150 healthy individuals. Detection of genetic polymorphism of SNP of 10 gene variants involved with NAFLD without and with fibrosis, gene discovery and molecular diagnosis of dyslipidemia using next-generation sequencing and whole-exome sequencing (genomics), the expression level for the top 5 of 168-panel genes of plasma miRNAs (epi-genomics), the glycosylation pattern of five glycoproteins (proteomics), salivary analysis of ten microbiomes and five microbial-related metabolites (metabolomics) will be investigated. Eventually, the development of precision therapies to target NAFLD without and with fibrosis and possibly reverse fibrosis could be achieved.

Study Overview

• Status: Not yet recruiting
• Conditions: Non-Alcoholic Fatty Liver Disease; Non Alcoholic Steatohepatitis
• Intervention / Treatment: Diagnostic test: Genomics (DNA Extraction); Diagnostic test: Epi-genomics; Diagnostic test: Proteomics (Glycoproteomics); Diagnostic test: Salivary Metabolomics; Behavioral: Individualized counselling for behavioural modification

Detailed Description

The national treatment program intended to provide a cure for Egyptian HCV-infected patients was the sparking light toward an HCV-free and healthy liver among the Egyptian population. However, another rapidly evolving liver disease has been emerging with an increase in both mortality and morbidity, and an estimated prevalence of 25-35% across the globe; non-alcoholic fatty liver disease (NAFLD). The highest rate of NAFLD is reported from the Middle East (32%). The prevalence of NAFLD in the general population increases with age; from 3% in children, 5% in teenagers, 18% between 20 and 40 years, 39% in those aged 40 to 50 years, and to over 40% in those greater than 70 years.

NAFLD is a metabolic disorder, whose spectrum progresses from simple steatosis to non-alcoholic steatohepatitis (NASH) and liver fibrosis, potentially leading to cirrhosis, hepatocellular carcinoma, and liver failure. Accordingly, NASH is considered a severe form of NAFLD. Given the association between NAFLD and the growing global epidemics of obesity, type 2 diabetes, sedentary lifestyles, dyslipidemia, and unhealthy dietary patterns, the prevalence of NAFLD is expected to increase. Therefore, NAFLD is a major clinical and economic burden on the world's healthcare systems.

Although liver biopsy is the reference standard for the assessment of fibrosis associated with NASH, the inherent limitations of an invasive procedure, and the need for repeat sampling, have led to the development of several non-invasive tests (NITs) as alternatives to liver biopsy. The current NITs were used for the diagnosis of advanced fibrosis in patients with NAFLD (5), Such NITs mostly include biological (serum biomarker algorithms) or physical (imaging assessment of tissue stiffness) assessments. However, currently available NITs have several limitations, such as variability, inadequate accuracy, and risk factors for error. The current NITs were used not only to diagnose significant fibrosis in chronic hepatitis C but also the diagnosis of advanced fibrosis in patients with NAFLD/NASH.

In low-resource countries, despite the high prevalence of NAFLD and that its early stages are reversible with diet and lifestyle modifications, the availability of NITs is likely to be limited, especially the more expensive imaging-based tests. Blood-based biomarkers are therefore attractive, but those available to date have only moderate diagnostic accuracy. Furthermore, only a minority of NAFLD cases are diagnosed and correctly treated as detecting early stages is hindered by a lack of non-invasive reliable, and validated methods of early diagnosis. In addition, there are few options available for the management of NASH and no current FDA-approved therapies for NAFLD.

To date, the pathogenesis of NAFLD is not fully clarified. NAFLD is thought to be involved in complex interactions among diet, genetic susceptibility, and gut microbiota (6). At the same time, the role of gut microbiota and microbial metabolites in NAFLD has attracted more attention. Gut microbiota regulates the development and progression of NAFLD on the basis of the gut-liver axis. Future targeted treatment strategies based on the pathogenic pathways are accordingly needed to develop an effective treatment for patients with NASH.

Broadly speaking, the scientific fields associated with measuring the biological molecules in a high-throughput way are called "omics". Although, ongoing technological advances in omics technologies such as genomics, proteomics, and metabolomics hold great promise for the discovery of useful non-invasive biomarkers and increased pathophysiological understanding of NAFLD and NASH diagnosis, prognosis, and drug response. The majority applied one omics technique. Advances in human genetics present new opportunities to address the urgent need for NASH therapeutics, based on an improved understanding of the interaction between the genetic and environmental risk factors for the development of NASH. MicroRNAs (miRNAs were reported to be closely related to NAFLD by targeting genes involved in lipid metabolism and pro-inflammatory factors which are related to the pathogenesis of NAFLD. In addition, many glycoproteins have been linked with the diagnosis of liver disorders given that the majority of serum glycoproteins are synthesized in the liver. Unfortunately, a few pioneering studies have successfully applied multi-omics technologies to investigate NAFLD/NASH, none of which was done among the Egyptian population.

The investigators aimed at developing a multi-omics composite predictive biomarker panel to be used as an Egyptian scoring system to improve the predictive power of the diagnosis of NAFLD and limit its progression to NASH, compared with the traditional markers that usually focus on a single aspect of the disease. Such predictive biomarkers can also benefit the clinical management of NAFLD to limit its progression to NASH.

Specific objectives:

1. To identify the functional variants causing dyslipidemia and its causative genes mutation on a subsample of participants (30 patients) using next-generation sequencing (NGS).
2. To identify and verify the most significant 10 genes (previously identified to be associated with NAFLD/NASH in genome-wide analyses) among the Egyptian population: PNPLA3 rs738409, PNPLA3 rs6006460, FDFT1 rs2645424, COL13A1 rs1227756, NCAN rs2228603, LYPLAL1 rs12137855, GCKR rs780094, PPP1R3B rs4240624, PPAR rs1800234 and MTTP rs1800591 using TaqMan SNP Genotyping Assay
3. To outline the possible association of genetic variations with the currently used treatment for NAFLD with and without fibrosis (e.g vitamin E, vitamin c, vitamin D; metformin for children and pioglitazone for adults…)
4. To identify the expression level of plasma miRNAs (the top 5 of 168-panel genes of plasma miRNAs) by PCR array as per the studied groups
5. To assess the potential of altered miRNAs expression profile associated with currently used drug for the treatment of NAFLD without or with fibrosis.
6. To identify the glycosylation profile of both N- and O-glycoproteins proved to be linked with NAFLD/NASH (transferrin, apolipoprotein C III (apoC III), haptoglobin, Mac2 binding protein, IgG) -) among Egyptian patients with NAFLD without or with fibrosis
7. To assess the relationship between the glycosylation pattern of the studied glycoproteins in response to currently used drug for NAFLD WITH AND WITHOUT FIBROSIS treatment.
8. To identify the bacterial isolates among the Egyptian populations that are linked to NAFLD patients without and with fibrosis and controls (out of 10 bacterial isolates) using -Rapid RT-PCR test for 16S rRNA gene amplicon library preparation and sequencing
9. To identify the top five salivary detected microbiome-related metabolites by comparing their concentrations for NAFLD without and with fibrosis and control using Gas Chromatography-Mass Spectrometer (GC-MS) Analysis
10. To assess the association of some known meta-genomic functions using Commercial ELISA kits. (salivary concentrations of the lactoferrin, lipopolysaccharide, and immunoglobulin A for detecting NAFLD without and with fibrosis.
11. To determine whether combinations of the detected salivary metabolites could be used as a biomarker signature for detecting NAFLD without and with fibrosis.
12. To identify the interaction and influence of the epidemiological, dietary, lifestyle on the studied multi-omics biomarkers and on the clinical presentation of NAFLD without and with fibrosis.

Research Methodology and tools

This study is a cross-sectional exploratory study conducted along 24 months tools:

1. Baseline Assessment Questionnaire:

   1.1 Assessment of the sociodemographic characteristics, detailed history taking, and other known risk factors (oral hygiene….), family pedigree construction up to three generations to diagnose the risk of genetic and familial causes for NAFLD and NASH 1.2 Nutritional and dietary behavioral assessment with anthropometric measurements using Diet Quality index assessment questionnaire, beverages intake

2. Multi-omics biomarkers in blood and alive will be investigated in this study. The approach is based on detecting new molecular pathogeneses and new genes for discovering Egyptian-related biomarkers of the studied multi-omics markers.

   2.1 Blood samples: Genomics

   2.1.1.1 Detection of genes involved in Dyslipidemia: Identification of functional variant(s) that is responsible for Dyslipidemia using next-generation sequencing (NGS) panels of Dyslipidemia main genes; (LDLR), (APOB), (PCSK9) and (LDLRAP). This technique can help us to identify novel dyslipidemia-related variants and those related commonly to the Egyptian population. This will be applied to 30 participants only diagnosed to have dyslipidemia.

   2.1.1.2 Detection of gene polymorphism for NAFLD and NASH by "TaqMan SNP Genotyping Assay" for the whole genome previously identified in genome-wide analyses to be linked to NAFLD/NASH (to be verified among the Egyptian population) PNPLA3 rs738409, PNPLA3 rs6006460, FDFT1 rs2645424, COL13A1 rs1227756, NCAN rs2228603, LYPLAL1 rs12137855, GCKR rs780094, PPP1R3B rs4240624, PPAR rs1800234 and MTTP rs1800591: from all participants

   • DNA will be extracted from the peripheral blood using a DNA extraction kit supplied by Qiagen, the USA using Nanodropper 2000 (ThermoScientific).
   • SNP Genotyping will be performed using the Roche real-time PCR system (light cycler 480) with TaqMan allelic discrimination assay (Applied Biosystems, USA).

   2.1.2 Epi-genomics Expression profiling of plasma microRNAs

   • Expression analysis of top 5 miRNAs: The top 5 altered miRNAs will be selected to be analyzed in all patients and controls by real-time PCR using mercury LNA SYBR Green PCR kit (Qiagen). Fold change of miRNA will be calculated using 2-∆Ct method.

   2.1.3 Glycoproteomics: identifying the glycosylation pattern transferrin, apolipoprotein C III (apoC III), haptoglobin, Mac2 binding protein, IgG (Santa Cruz, USA). T

   2.2 Saliva samples: 2.2.1 Salivary Metabolomics 2.2.2 Metabolites Identification using Gas Chromatography-Mass Spectrometer (GC-MS) Analysis: assessing the concentration of the top five identified microbial-related metabolites (which will be found in at least 85% of samples) as metabolomics biomarkers among all participants will be investigated using GC-MS analysis.

   2.2.3 Predictive analysis of some known meta-genomic functions. The salivary concentrations of the lactoferrin, lipopolysaccharide (LPS), and immunoglobulin A (IgA) will be determined for all participants.

3. Behavioral modification through individual counseling

• Study Type: Observational
• Enrollment (Anticipated): 450

Contacts and Locations

• Study Contact: Name: Ammal M Metwally, PhD (MD); Phone Number: +201222280640; Email: ammal_mok@yahoo.com
• Study Contact Backup: Name: Iman H Kamel, PhD (MD); Phone Number: +201222906160; Email: imankamelh@gmail.com

Study Locations

• Egypt
  • Al Jizah
    • Giza, Al Jizah, Egypt, 12411
      • National Research Centre

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 12 years to 60 years (Child, Adult)
• Accepts Healthy Volunteers: N/A
• Genders Eligible for Study: All

• Sampling Method: Probability Sample

Study Population

The study will be carried out on 450 individuals, 225 of them will be children aged 12-18 years and 225 will be adults in the age range 30-60 years according to pre-set inclusion and exclusion criteria. The enrolled adults and children will be diagnosed according to the recommendation of EASL; AASLD (13) and ESPGHAN Hepatology Committee (14) Respectively

Description

Inclusion Criteria:

• Age: 30-60 years for adults and 12-18 years for children
• BMI: ≥ 25 for adults, BMI: ≥ 85th and <94th percentile for overweight and ≥95th percentile for obese children
• Pre-diabetics and type 2 diabetes
• Dyslipidemia
• Hypertension
• Family history of NASH

Exclusion Criteria:

• • Alcohol consumption

  • Type 1 diabetes
  • Other chronic liver diseases
  • Malignant diseases

Study Plan

How is the study designed?

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
NAFLD group without fibrosis; Group diagnosed to have NAFLD without fibrosis according to the recommendation of EASL; AASLD (13) and ESPGHAN Hepatology Committee (14), (75 adults aged 30-60 years, 75 children aged 12-18 years) ,	Diagnostic test: Genomics (DNA Extraction); Blood samples for detection of: Genes for Dyslipidemia BY WES and NGS (4 GWA) (30 cases) Genes polymorphism for NAFLD/NASH BY TaqMan SNP Genotyping Assay on 10 GWAS; Diagnostic test: Epi-genomics; blood samples for detection of Expression profiling of plasma microRNAs; PCR array to determine the altered miRNAs for 168 plasma miRNAs (12 cases); Expression analysis of top 5 altered miRNAs (All); Diagnostic test: Proteomics (Glycoproteomics); blood samples for Identifying glycosylation pattern of five glycoproteins linked with NAFLD/NASH (transferrin, apoC III, haptoglobin, Mac2 binding protein, IgG); Diagnostic test: Salivary Metabolomics; Salivary Samples for detecting Salivary Metabolomics; Genome analysis (Identification of microbial strains common among Egyptians BY apid RT-PCR (DNA sequencing: 16S rRNA gene amplicons); Microbiome related Metabolites Identification using GC-MS Analysis (the top 5 identified microbial-related metabolites) as metabolomics; Meta-genomic assess of three microbiome-related metabolites; lactoferrin, (LPS), (IgA) BY Commercial ELISA kits; Behavioral: Individualized counselling for behavioural modification; Individualized counselling for behavioral modification (3 sessions): Nutritional education, Promotion of physical activities and Cognitive & Psychological support
NAFLD group with fibrosis (potential NASH); Group diagnosed to have NAFLD with fibrosis according to the recommendation of EASL; AASLD (13) and ESPGHAN Hepatology Committee (14), (75 adults aged 30-60 years, 75 children aged 12-18 years),	Diagnostic test: Genomics (DNA Extraction); Blood samples for detection of: Genes for Dyslipidemia BY WES and NGS (4 GWA) (30 cases) Genes polymorphism for NAFLD/NASH BY TaqMan SNP Genotyping Assay on 10 GWAS; Diagnostic test: Epi-genomics; blood samples for detection of Expression profiling of plasma microRNAs; PCR array to determine the altered miRNAs for 168 plasma miRNAs (12 cases); Expression analysis of top 5 altered miRNAs (All); Diagnostic test: Proteomics (Glycoproteomics); blood samples for Identifying glycosylation pattern of five glycoproteins linked with NAFLD/NASH (transferrin, apoC III, haptoglobin, Mac2 binding protein, IgG); Diagnostic test: Salivary Metabolomics; Salivary Samples for detecting Salivary Metabolomics; Genome analysis (Identification of microbial strains common among Egyptians BY apid RT-PCR (DNA sequencing: 16S rRNA gene amplicons); Microbiome related Metabolites Identification using GC-MS Analysis (the top 5 identified microbial-related metabolites) as metabolomics; Meta-genomic assess of three microbiome-related metabolites; lactoferrin, (LPS), (IgA) BY Commercial ELISA kits; Behavioral: Individualized counselling for behavioural modification; Individualized counselling for behavioral modification (3 sessions): Nutritional education, Promotion of physical activities and Cognitive & Psychological support
Healthy group; Healthy control group age and sex-matched with the previous group (75 adults aged 30-60 years, 75 children aged 12-18 years),	Diagnostic test: Genomics (DNA Extraction); Blood samples for detection of: Genes for Dyslipidemia BY WES and NGS (4 GWA) (30 cases) Genes polymorphism for NAFLD/NASH BY TaqMan SNP Genotyping Assay on 10 GWAS; Diagnostic test: Epi-genomics; blood samples for detection of Expression profiling of plasma microRNAs; PCR array to determine the altered miRNAs for 168 plasma miRNAs (12 cases); Expression analysis of top 5 altered miRNAs (All); Diagnostic test: Proteomics (Glycoproteomics); blood samples for Identifying glycosylation pattern of five glycoproteins linked with NAFLD/NASH (transferrin, apoC III, haptoglobin, Mac2 binding protein, IgG); Diagnostic test: Salivary Metabolomics; Salivary Samples for detecting Salivary Metabolomics; Genome analysis (Identification of microbial strains common among Egyptians BY apid RT-PCR (DNA sequencing: 16S rRNA gene amplicons); Microbiome related Metabolites Identification using GC-MS Analysis (the top 5 identified microbial-related metabolites) as metabolomics; Meta-genomic assess of three microbiome-related metabolites; lactoferrin, (LPS), (IgA) BY Commercial ELISA kits; Behavioral: Individualized counselling for behavioural modification; Individualized counselling for behavioral modification (3 sessions): Nutritional education, Promotion of physical activities and Cognitive & Psychological support

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Dyslipidemia-related variants related commonly to the Egyptian population	NGS panels of dyslipidemia main genes; (LDLR), (APOB), (PCSK9), and (LDLRAP) will be customized by Illumina to screen for mutations in 30 participants (as detected by OR in relation to controls).	12 months after the start of the recruitment
The most significant predisposing or protective genetic variants out of the studied risk and protective alleles associated with NAFLD without and with fibrosis in the Egyptian population.	The identification of people who carry a specific genetic variant predisposing them to NAFLD with fibrosis Through gene polymorphisms (as detected by OR in relation to controls)	12 months after the start of the recruitment
The expression level of altered plasma mRNAs detected among the Egyptian population	Expression profiling of plasma microRNAs expression profiling will be performed by locked nucleic acid PCR array for high plasma miRNAs. This will be applied to 2 subjects from each group (a total of 12 subjects).	12 months after the start of the recruitment
The glycosylation profile of the studied N- and O-glycoproteins among Egyptians	identifying the glycosylation pattern transferrin, apolipoprotein C III (Apoc III), haptoglobin, Mac2 binding protein, IgG (Santa Cruz, USA). The protein bands were visualized as a chemiluminescence reaction using ECL (Novex, Invitrogen, Thermo Scientific, US), and the images will be taken using a CDD camera.	12 months after the start of the recruitment
Differences in the compositions and types of the bacterial isolates among the Egyptian populations that are linked to NAFLD patients without and with fibrosis vs. controls	(out of 10 bacterial isolates) using -Rapid RT-PCR test for 16S rRNA gene amplicon library preparation and sequencing	12 months after the start of the recruitment
Concentration level of the high salivary detected microbiome-related metabolites	The salivary concentrations of the high salivary detected microbiome-related metabolites using Gas Chromatography-Mass Spectrometer (GC-MS) Analysis and their predictive value (Odds Ratio)	12 months after the start of the recruitment
Production of a novel non-invasive biomarker panel to be used for NAFLD without and with fibrosis prediction and diagnosis.	Using a logistic regression prediction model for the identification of significant multi-omics biomarkers will help in the development of a unique Egyptian scoring system.	24 months from the start of the study

Collaborators and Investigators

• Sponsor: National Research Centre, Egypt
• Investigators: Principal Investigator: Ammal M Metwally, National Research Centre of Egypt

Publications and helpful links

General Publications

• Chow, S.C.; Shao, J.; Wang, H. 2003. Sample Size Calculations in Clinical Research. Marcel Dekker. New York.
• Leoni S, Tovoli F, Napoli L, Serio I, Ferri S, Bolondi L. Current guidelines for the management of non-alcoholic fatty liver disease: A systematic review with comparative analysis. World J Gastroenterol. 2018 Aug 14;24(30):3361-3373. doi: 10.3748/wjg.v24.i30.3361.
• Newby PK, Hu FB, Rimm EB, Smith-Warner SA, Feskanich D, Sampson L, Willett WC. Reproducibility and validity of the Diet Quality Index Revised as assessed by use of a food-frequency questionnaire. Am J Clin Nutr. 2003 Nov;78(5):941-9. doi: 10.1093/ajcn/78.5.941.
• Ahmed MH, Noor SK, Bushara SO, Husain NE, Elmadhoun WM, Ginawi IA, Osman MM, Mahmoud AO, Almobarak AO. Non-Alcoholic Fatty Liver Disease in Africa and Middle East: An Attempt to Predict the Present and Future Implications on the Healthcare System. Gastroenterology Res. 2017 Oct;10(5):271-279. doi: 10.14740/gr913w. Epub 2017 Oct 26.
• Gan L, Chitturi S, Farrell GC. Mechanisms and implications of age-related changes in the liver: nonalcoholic Fatty liver disease in the elderly. Curr Gerontol Geriatr Res. 2011;2011:831536. doi: 10.1155/2011/831536. Epub 2011 Sep 12.
• Perakakis N, Stefanakis K, Mantzoros CS. The role of omics in the pathophysiology, diagnosis and treatment of non-alcoholic fatty liver disease. Metabolism. 2020 Oct;111S:154320. doi: 10.1016/j.metabol.2020.154320. Epub 2020 Jul 23.
• Giraudi PJ, Stephenson AM, Tiribelli C, Rosso N. Novel high-throughput applications for NAFLD diagnostics and biomarker discovery. Hepatoma Res 2021; 7:2
• Patel K, Sebastiani G. Limitations of non-invasive tests for assessment of liver fibrosis. JHEP Rep. 2020 Jan 20;2(2):100067. doi: 10.1016/j.jhepr.2020.100067. eCollection 2020 Apr.
• Beattie M, Dhawan A, Puntis JWL, et al., Non alcoholic fatty liver disease, chapter 61;520-528 Oxford Specialist Handbook of Paediatric Gastroenterology, Hepatology, and nutrition. Oxford university press, 3rd edition,2018
• Rosenbaum J, Usyk M, Chen Z, Zolnik CP, Jones HE, Waldron L, Dowd JB, Thorpe LE, Burk RD. Evaluation of Oral Cavity DNA Extraction Methods on Bacterial and Fungal Microbiota. Sci Rep. 2019 Feb 6;9(1):1531. doi: 10.1038/s41598-018-38049-6.
• Younossi ZM, Reyes MJ, Mishra A, Mehta R, Henry L. Systematic review with meta-analysis: non-alcoholic steatohepatitis - a case for personalised treatment based on pathogenic targets. Aliment Pharmacol Ther. 2014 Jan;39(1):3-14. doi: 10.1111/apt.12543. Epub 2013 Nov 10.
• Committee on the Review of Omics-Based Tests for Predicting Patient Outcomes in Clinical Trials; Board on Health Care Services; Board on Health Sciences Policy; Institute of Medicine; Micheel CM, Nass SJ, Omenn GS, editors. Evolution of Translational Omics: Lessons Learned and the Path Forward. Washington (DC): National Academies Press (US); 2012 Mar 23. Available from http://www.ncbi.nlm.nih.gov/books/NBK202168/
• Xin S, Zhan Q, Chen X, Xu J, Yu Y. Efficacy of serum miRNA test as a non-invasive method to diagnose nonalcoholic steatohepatitis: a systematic review and meta-analysis. BMC Gastroenterol. 2020 Jun 12;20(1):186. doi: 10.1186/s12876-020-01334-8.
• de Haan N, Falck D, Wuhrer M. Monitoring of immunoglobulin N- and O-glycosylation in health and disease. Glycobiology. 2020 Mar 20;30(4):226-240. doi: 10.1093/glycob/cwz048.
• Neuman MG, Cohen LB, Nanau RM. Biomarkers in nonalcoholic fatty liver disease. Can J Gastroenterol Hepatol. 2014 Dec;28(11):607-18. doi: 10.1155/2014/757929.
• Vajro P, Lenta S, Socha P, Dhawan A, McKiernan P, Baumann U, Durmaz O, Lacaille F, McLin V, Nobili V. Diagnosis of nonalcoholic fatty liver disease in children and adolescents: position paper of the ESPGHAN Hepatology Committee. J Pediatr Gastroenterol Nutr. 2012 May;54(5):700-13. doi: 10.1097/MPG.0b013e318252a13f.
• Cheah MC, McCullough AJ, Goh GB. Current Modalities of Fibrosis Assessment in Non-alcoholic Fatty Liver Disease. J Clin Transl Hepatol. 2017 Sep 28;5(3):261-271. doi: 10.14218/JCTH.2017.00009. Epub 2017 Jun 24.
• Castera L, Friedrich-Rust M, Loomba R. Noninvasive Assessment of Liver Disease in Patients With Nonalcoholic Fatty Liver Disease. Gastroenterology. 2019 Apr;156(5):1264-1281.e4. doi: 10.1053/j.gastro.2018.12.036. Epub 2019 Jan 18.
• Matsuda K. PCR-Based Detection Methods for Single-Nucleotide Polymorphism or Mutation: Real-Time PCR and Its Substantial Contribution Toward Technological Refinement. Adv Clin Chem. 2017;80:45-72. doi: 10.1016/bs.acc.2016.11.002. Epub 2017 Jan 4.
• Papari E, Noruzinia M, Kashani L, Foster WG. Identification of candidate microRNA markers of endometriosis with the use of next-generation sequencing and quantitative real-time polymerase chain reaction. Fertil Steril. 2020 Jun;113(6):1232-1241. doi: 10.1016/j.fertnstert.2020.01.026.

Study record dates

Study Major Dates

• Study Start (Anticipated): August 1, 2022
• Primary Completion (Anticipated): December 30, 2023
• Study Completion (Anticipated): April 30, 2024

Study Registration Dates

• First Submitted: February 28, 2022
• First Submitted That Met QC Criteria: March 17, 2022
• First Posted (Actual): March 29, 2022

Study Record Updates

• Last Update Posted (Actual): June 3, 2022
• Last Update Submitted That Met QC Criteria: June 1, 2022
• Last Verified: June 1, 2022

More Information

Terms related to this study

• Keywords: genomics; non-alcoholic steatohepatitis; metabolomics; proteomics; non-alcoholic fatty liver disease (NAFLD); epi-genomics
• Additional Relevant MeSH Terms: Digestive System Diseases; Liver Diseases; Fatty Liver; Non-alcoholic Fatty Liver Disease
• Other Study ID Numbers: 20211129

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: All IPD that underlie results in a publication will be shared
• IPD Sharing Time Frame: After the end of the project implementation
• IPD Sharing Access Criteria: the data will be accessed through a drive with a link
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; CSR

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No