Back

Fibrosis status, extra-hepatic multimorbidity and all-cause mortality in 53093 women and 74377 men with MASLD in UK Biobank

Feng, Q.; Izzi-Engbeaya, C.; Manousou, P.; Woodward, M.

2025-03-14 gastroenterology
10.1101/2025.03.09.25323329 medRxiv
Show abstract

IntroductionPeople with MASLD had higher risk of extrahepatic multimorbidity, and fibrosis is the strongest prognostic factor for mortality in MASLD. This study aimed to investigate how fibrosis was associated with multimorbidity and their relationships with all-cause mortality. MethodsWe utilized data from the UK Biobank. MASLD was identified via a Fatty Liver Index [≥] 60 and the presence of cardiometabolic risk factors. The fibrosis-4 (FIB4) score was used to measure liver fibrosis. Multimorbidity was defined as having two or more long-term conditions (LTCs) from a prespecified list of 47 LTCs. Logistic regressions estimated the cross-sectional association between FIB4 scores and multimorbidity prevalence, while Cox models assessed the prospective association between FIB4 scores, multimorbidity and mortality. ResultsWe included 127470 participants with MASLD (41.7% female, age 57.4 years, 21.3% with multimorbidity, 2.2% with high FIB4 scores). 14471 deaths were recorded during 13-year follow-up. Compared to low FIB4 scores, high FIB4 scores were associated with 41% higher prevalence of multimorbidity (OR 1.41 (95%CI 1.30-1.54)), and 94% higher all-cause mortality (HR 1.94 (95%CI 1.77-2.13)), with multimorbidity explaining 10% of this association, primarily driven by contributions from cardiovascular diseases, extrahepatic cancers, and chronic kidney disease. ConclusionFIB4 scores were positively associated with higher multimorbidity and mortality in MASLD patients. Multimorbidity explained 10% the relationship between fibrosis and mortality, with cardiovascular diseases, cancers and chronic kidney disease contributing notably to this mediation. These findings underscore the importance of managing both fibrosis and multimorbidity in MASLD patients.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"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.