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Published on: April 16, 2019
Fibrotic NASH Index (FNI) Is Associated with Long-Term Mortality in Individuals with Type 2 Diabetes and MASLD
Ilaria Barchetta1, Flavia Agata Cimini1, Sara Dule1
1Department of Experimental Medicine, Sapienza University, Rome, Italy.
Background And Aim:
Metabolic dysfunction-associated steatotic liver disease (MASLD) frequently coexists with type 2 diabetes (T2D) and increases cardiovascular disease (CVD) risk, with hepatic fibrosis being the main determinant of mortality. Existing non-invasive fibrosis scores often include age and may be less informative in early-stage disease or long-term follow-up. This study investigated the association between the Fibrotic NASH Index (FNI), an age-independent marker and long-term all-cause mortality in T2D.
Methods:
We conducted a retrospective longitudinal study in a cohort of 174 individuals with T2D (baseline age 67.6 ± 11.4 years; BMI 29.3 ± 5.2 kg/m2; HbA1c 7.1% ± 1.7%) enrolled in 2000-2001. Complete mortality data at 20 years were available for all the study participants. Hepatic steatosis was assessed by ultrasound, and fibrosis risk was estimated using FNI; FIB-4 and APRI were also calculated. Baseline metabolic, biochemical and clinical data were recorded.
Results:
At baseline, steatosis was present in 78% and high fibrosis risk in 44% of participants. After 20 years, mortality reached 39.7%. The FNI-defined fibrosis risk was significantly associated with the vital status at 20 years in both univariate and multivariable models adjusted for age, sex, BMI, lipids, renal function, steatosis, diabetes' duration and CVD history (OR 11.75; 95% CI: 2.11-65.50; p = 0.005), whereas neither FIB4 nor APRI retained a significant association after multivariable adjustment (FIB-4: OR 1.50; 95% CI: 0.52-4.37; p = 0.45; APRI: OR 1.49; 95% CI: 0.29-7.53; p = 0.63).
Conclusion:
FNI-defined liver fibrosis risk is independently associated with long-term mortality in individuals with T2D. Incorporating non-invasive, age-independent fibrosis assessment may help improve early risk stratification and guide personalised management in dysmetabolic populations.
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