Genetic susceptibility versus fibrosis progression in North Indian MASLD: distinct roles of APOC3 and PNPLA3 in a candidate gene study
Tomar, N.; Choudhury, S.; Arora, A.; Sharma, P.; Vaibhav, R.; Hasan, R.; Jan, S.; Kaur, R.; Rajput, T.; Lomada, M. S.; Pemmasani, S. K.; Kumar, A.
Show abstract
Background and AimMASLD affects 30-38% of Indian adults, yet the contribution of genetic risk variants to disease susceptibility and fibrosis progression remains poorly characterised. We investigated the association of 12 candidate SNPs with MASLD susceptibility and fibrosis severity in North Indian patients, benchmarking allele frequencies against IndiGenomes and global populations. MethodsSixty-nine MASLD patients (75.4% male; median BMI 29.8 kg/m{superscript 2}) from a tertiary care liver clinic in New Delhi were genotyped for 12 SNPs using Illumina custom BeadChip array and Sanger sequencing. Patients were stratified by liver stiffness measurement (LSM): significant fibrosis ([≥]8 kPa, n=38) versus no significant fibrosis (<8 kPa, n=31). Allele frequencies were compared with IndiGenomes ([~]1,020 Indian individuals) and 1000 Genomes populations. ResultsPNPLA3 rs738409 G allele was the strongest within-cohort predictor of significant fibrosis (allelic OR 2.89, 95% CI 1.35-6.19, P=0.006; dominant model OR 3.94, P=0.008), with carriers demonstrating higher LSM (median 15.6 vs. 7.5 kPa, P=0.005). SAMM50 rs3761472 (OR 2.12, P=0.065) and FTO rs9939609 (OR 2.08, P=0.089) showed non-significant trends. In the population-level comparison, APOC3 rs2854116 T allele was the only variant significantly enriched after Bonferroni correction (64.0% vs. 47.9%; OR 1.93, 95% CI 1.35-2.77, P<0.001), followed by PNPLA3 (33.3% vs. 24.1%, OR 1.57, P=0.019) and SAMM50 (31.2% vs. 22.6%, OR 1.55, P=0.028). Notably, APOC3 showed no association with fibrosis (OR 0.96, P=1.000), suggesting a role in susceptibility rather than progression. All SNPs were in Hardy-Weinberg equilibrium. ConclusionsThis study reveals a dissociation between genetic determinants of MASLD susceptibility and fibrosis progression in North Indian patients. APOC3 rs2854116 predisposes to MASLD at the population level, while PNPLA3 rs738409 drives fibrosis severity within established disease, underscoring the need for ancestry-specific genetic risk stratification. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/26347059v1_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@a07808org.highwire.dtl.DTLVardef@12882adorg.highwire.dtl.DTLVardef@9b33a1org.highwire.dtl.DTLVardef@15aa5e8_HPS_FORMAT_FIGEXP M_FIG C_FIG
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Identification and functional characterisation of a rare MTTP variant underlying hereditary non-alcoholic fatty liver disease 96%
- Comprehensive lipidomics reveals reduced hepatic lipid turnover in NAFLD during alcohol intoxication 93%
- p53 and TIGAR promote redox control to protect against metabolic dysfunction-associated steatohepatitis 92%
Similar papers in this journal
Similar papers in this journal
- Liver disease is a significant risk factor for cardiovascular outcomes - a UK Biobank study 95%
- rs641738C>T near MBOAT7 is positively associated with liver fat, ALT, and histological severity of NAFLD: a meta-analysis 95%
- Mendelian Randomization Analysis Dissects the Relationship between NAFLD, T2D, and Obesity and Provides Implications to Precision Medicine 94%
Similar papers in this journal
- Genome-Wide Association Study of Non-Alcoholic Fatty Liver Disease Identifies Association with Apolipoprotein E 96%
- Hepatic MCPIP1 protein levels are reduced in NAFLD patients and are predominantly expressed in cholangiocytes and liver endothelium 93%
- The role of intestine in metabolic dysregulation in murine Wilson disease 92%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.