2482-P: Metabolic Obesity Phenotypes and Advanced Liver Fibrosis: A Cross-Sectional Transient Elastography Study

Introduction and Objective: Body mass index (BMI) alone may miss obesity risk; ICMR-INDIAB study supports metabolic phenotyping into four groups based on their metabolic and BMI status: metabolically healthy non-obese (MHNO), metabolically obese non-obese (MONO), metabolically obese obese (MOO), and metabolically healthy obese (MHO). We assessed phenotype distribution and association with liver fibrosis stage using MHNO as a reference. Methods: We conducted a cross-sectional analysis of an adult cohort with complete demographic, anthropometric, and biochemical profiles, all of whom underwent vibration-controlled transient elastography. The participants were categorized into four groups as per the ICMR-INDIAB study. Fibrosis staging was determined via liver stiffness measurement (advanced fibrosis > 9.5 kPa). We analyzed the demographic data, sex distribution, and fibrosis staging across categories. Odds ratios (ORs) for advanced fibrosis were calculated using MHNO as the baseline, applying Haldane-Anscombe continuity corrections where necessary. Results: In this cross-sectional cohort of 349 adults (mean age 51.1±11.1 years; 47.3% men), metabolic obesity phenotypes were MHNO n=74 (21.2%), MONO, n=101 (28.9%), MHO, n=2 (0.6%), and MOO, n=172 (49.3%). Advanced fibrosis occurred in 52.9% of participants with MOO (91/172) and in 1/2 with MHO, but in 0/74 with MHNO and 0/101 with MONO. Compared with MHNO, MOO had markedly higher odds of advanced fibrosis (crude OR ∞; Haldane-Anscombe-corrected OR ≈167, 95% CI ≈10.2-2743; p<0.001). In fibrosis staging, MHNO and MONO were entirely F0-F1 (<7.0 kPa), whereas MOO was predominantly ≥F2 (F2 44.2%, F3 23.8%, F4 31.4%). Conclusion: In this pioneer study with people with diabetes, we found advanced fibrosis on transient elastography was concentrated in metabolically unhealthy obesity, with none observed in non-obesity groups. Combining metabolic risk status with obesity classification may help target fibrosis screening and risk-reduction efforts. Disclosure A. Tewari: None. J. Tewari: None. V. Tewari: None. N. Verma: None. A. Maheshwari: None.