Combining Gene Variants to Improve Risk Prediction for Metabolic (Dysfunction)- Associated Fatty Liver Disease and Its Progression to Cirrhosis in Indian Individuals With Type 2 Diabetes

March 3, 2024 updated by: Dr Mohammad Shafi Kuchay, Medanta, The Medicity, India

Combining Gene Variants to Improve Risk Prediction for Metabolic (Dysfunction)- Associated Fatty Liver Disease and Its Progression to Cirrhosis in Indian Individuals With Type 2 Diabetes: a Cross-sectional Study

Type 2 diabetes and metabolic (dysfunction)-associated fatty liver disease (MAFLD) often exist together. The prevalence of MAFLD is about 15-30% in healthy people and around 60-70% in people with type 2 diabetes. Moreover, type 2 diabetes accelerates the progression of liver disease in MAFLD.

MAFLD is a spectrum of liver conditions, ranging from simple fatty liver (low risk for progression), progressing to steatohepatitis (MASH) with no or mild fibrosis, advanced liver fibrosis, cirrhosis, and hepatocellular carcinoma. Although diabetes is the strongest predictor of advanced fibrosis in MAFLD, however, only a small proportion of people with type 2 diabetes and MAFLD (about 5-7%) develop a clinically significant liver disease, but the burden of MAFLD is such that even a small proportion of patients developing cirrhosis will lead to a huge strain on the health care system in India. MAFLD is predicted to be the leading indication for liver transplantation in coming years. At present, MAFLD/MASH is the second most common indication for liver transplantation in the USA as well as in India.

The question is why around 5-7% patients amongst MAFLD population develop fibrosis and cirrhosis. A growing body of evidence suggest that the disease develops because of a complex process in which several factors, including genetic susceptibility and environmental insults, are involved. There are several gene variants that have been incriminated in the development and progression of MAFLD. The most common genes associated with MAFLD are PNPLA3, TM6SF2, GCKR, and MBOAT7. The loss-of-function gene variant HSD17B13 seems to protect from NAFLD. There are a few studies from India about the role of PNPLA3 and TM6SF2 in MAFLD. However, these studies used USG for the diagnosis of MAFLD, which does not provide any information regarding fibrosis of the liver. The data regarding other three genetic variants are scarce from Indian individuals.

Study Overview

Status

Recruiting

Conditions

Detailed Description

Type 2 diabetes and metabolic (dysfunction)-associated fatty liver disease (MAFLD) often exist together. The prevalence of MAFLD is about 15-30% in healthy people and around 60-70% in people with type 2 diabetes. Moreover, type 2 diabetes accelerates the progression of liver disease in MAFLD.

MAFLD is a spectrum of liver conditions, ranging from simple fatty liver (low risk for progression), progressing to steatohepatitis (MASH) with no or mild fibrosis, advanced liver fibrosis, cirrhosis, and hepatocellular carcinoma. Although diabetes is the strongest predictor of advanced fibrosis in MAFLD, however, only a small proportion of people with type 2 diabetes and MAFLD (about 5-7%) develop a clinically significant liver disease, but the burden of MAFLD is such that even a small proportion of patients developing cirrhosis will lead to a huge strain on the health care system in India. MAFLD is predicted to be the leading indication for liver transplantation in coming years. At present, MAFLD/MASH is the second most common indication for liver transplantation in the USA as well as in India.

The question is why around 5-7% patients amongst MAFLD population develop fibrosis and cirrhosis. A growing body of evidence suggest that the disease develops because of a complex process in which several factors, including genetic susceptibility and environmental insults, are involved. There are several gene variants that have been incriminated in the development and progression of MAFLD. The most common genes associated with MAFLD are PNPLA3, TM6SF2, GCKR, and MBOAT7. The loss-of-function gene variant HSD17B13 seems to protect from NAFLD. There are a few studies from India about the role of PNPLA3 and TM6SF2 in MAFLD. However, these studies used USG for the diagnosis of MAFLD, which does not provide any information regarding fibrosis of the liver. The data regarding other three genetic variants are scarce from Indian individuals.

  1. PNPLA3 rs738409 C/G polymorphism: The nonsynonymous rs738409 C/G variant in PNPLA3 (patatin-like phospholipase domain containing 3), which encodes the amino acid substitution I148M, is regarded as the major genetic component of MAFLD and MASH. The risk effect of rs738409 on developing fatty liver in the context of MAFLD is the strongest ever reported for a common variant modifying the genetic susceptibility of MAFLD (5.3% of the total variance). The rs738409 is not only significantly associated with the accumulation of fat in the liver (the lipid fat content in carriers of the GG homozygous genotype is 73% higher compared with that measured in the carriers of the CC genotype) but also with the histological disease severity and progression of MAFLD (odds ratio-OR 1.88 per G allele; 95% confidence interval-CI 1.03-3.43; GG vs. CC homozygous carriers OR 3.488, 95% CI 1.859-6.545). PNPLA3 is a lipase involved in hepatocellular lipid remodelling and retinol metabolism.
  2. TM6SF2: In 2014, two exome and genome wide association studies identified the rs58542926 C > T genetic variant of the transmembrane 6 superfamily member 2 gene (TM6SF2), which encodes the loss-of-function lysine (E) to glutamic acid (K) at position 167 substitution (E167K), as a determinant of hepatic fat content, serum aminotransferases, and lower serum lipoproteins. The same studies demonstrated that silencing of TM6SF2 reduces secretion of VLDL resulting in intrahepatic retention of triglycerides and steatosis in mice and in hepatocytes in vitro. TM6SF2 is involved in hepatic VLDL secretion.
  3. GCKR: A variant in GCKR locus (glucokinase regulatory gene) has recently gained attention of researchers due to its biological plausibility in the disease pathogenesis. Specifically, the missense variant rs780094 was associated with a modest risk of having a fatty liver. Interestingly, GCKR mutations have been involved in the maturity-onset diabetes in young individuals, given that diabetes/glucose intolerance/insulin resistance is a well-known risk factor for MAFLD. No Indian data.
  4. MBOAT7 rs641738 C/T polymorphism: The MBOAT7 polymorphism rs641738 was identified as a risk factor for MAFLD, especially in people of European decent. This association is mediated by lower hepatic protein expression of MBOAT7 resulting in changes in the hepatic phosphatidylinositol acyl-chain remodeling. The mechanism linking altered PI remodeling to MAFLD development and progression is not clear. The data form India is scarce.
  5. HSD17B13 rs72613567:TA: A gene variant that describes a link between hepatic phospholipids and the risk of advanced MAFLD is the splicing variant rs72613567 (T > TA) with an adenine insertion in HSD17B13 that encodes for the hepatic lipid droplet protein hydroxysteroid 17-beta dehydrogenase 13. The HSD17B13 rs72613567 variant leads to the synthesis of a truncated loss-of-function enzyme that protects against advanced MAFLD, MASH and fibrosis. Surprisingly, the gene variant does not influence the development of steatosis, as several studies showed no difference in the degree of steatosis between rs72613567 carriers and noncarriers; however, it decreases the risk of chronic liver damage in MAFLD patients we aimed to assess the role of these five gene variants (PNPLA3, TM6SF2, GCKR, MBOAT7 and HSD17B13) in the development of steatosis and fibrosis (as assessed by transient elastography), and correlation of these gene variants with body composition (body fat percentage, lean mass and bone mineral content- as assessed by dual-energy X-ray absorptiometry), in Indian individuals with type 2 diabetes

Study Type

Observational

Enrollment (Estimated)

1000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • India
    • Haryana
      • Gurgaon, Haryana, India, 122001
        • Recruiting
        • Medanta Division of Endocrinology & Diabetes
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Individuals with or without diabetes will be enrolled

Description

Inclusion Criteria:

Individuals with or without T2DM between ages 30 to 70 years

Exclusion Criteria:

Age below 30 years Patients with hepatitis B, hepatitis C or HIV disease Patients with significant alcohol intake (>14 drinks/week in men and >10 drinks/week in women).

Patients on corticosteroids and chemotherapeutic agents.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Individuals with or without diabetes

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Role of the PNPLA3, TM6SF2, GCKR, MBOAT7 and HSD17B13 genetic variants in the development and progression of MASLD.
Time Frame: One Year
To evaluate the role of the PNPLA3, TM6SF2, GCKR, MBOAT7 and HSD17B13 genetic variants in the development and progression of MASLD (steatosis, fibrosis, cirrhosis as measured by transient elastography, MRI-PDFF, dynamic MRI of the liver) in Indian individuals with type 2 diabetes
One Year

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Correlation between body fat percentage (DEXA-measured) and the genetic variants
Time Frame: One Year
To examine correlation between body fat percentage (DEXA-measured) and the genetic variants (PNPLA3, TM6SF2, GCKR, MBOAT7 and HSD17B13).
One Year
Correlation between lean body mass (as measured by DEXA) and the genetic variants
Time Frame: One Year
To examine correlation between lean body mass (as measured by DEXA) and the genetic variants (PNPLA3, TM6SF2, GCKR, MBOAT7 and HSD17B13).
One Year
Correlation between bone mineral content (DEXA-measured) and the genetic variants
Time Frame: One Year
To examine correlation between bone mineral content (DEXA-measured) and the genetic variants (PNPLA3, TM6SF2, GCKR, MBOAT7 and HSD17B13).
One Year
Correlation between fibrosis-4 score (FIB-4) and the genetic variants
Time Frame: One Year
To examine correlation between fibrosis-4 score (FIB-4) and the genetic variants (PNPLA3, TM6SF2, GCKR, MBOAT7 and HSD17B13).
One Year
Correlation between MASLD fibrosis score (NFS) and the genetic variants
Time Frame: One Year
To examine correlation between MASLD fibrosis score (NFS) and the genetic variants (PNPLA3, TM6SF2, GCKR, MBOAT7 and HSD17B13).
One Year
Correlation between serum creatinine levels (eGFR) and the genetic variants
Time Frame: One Year
To examine correlation between serum creatinine levels (eGFR) and the genetic variants (PNPLA3, TM6SF2, GCKR, MBOAT7 and HSD17B13).
One Year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Medanta, The Medicity, India

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 20, 2024

Primary Completion (Estimated)

January 1, 2025

Study Completion (Estimated)

January 10, 2025

Study Registration Dates

First Submitted

February 26, 2024

First Submitted That Met QC Criteria

February 26, 2024

First Posted (Actual)

March 1, 2024

Study Record Updates

Last Update Posted (Actual)

March 5, 2024

Last Update Submitted That Met QC Criteria

March 3, 2024

Last Verified

March 1, 2024

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • MMDEF001

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Clinical Trials on Type2diabetes

Search Similar Trials

Sponsors and Collaborators

Medical Conditions

Drug Interventions

CROs by country

CROs in Russia

Conditions

Rare Diseases

Drug Interventions

Dietary Supplements

Sponsor/Collaborators

Locations

3
Subscribe