Amelia Jones | 08/06/2026
This research was presented at the EASL Congress 2026 in Barcelona and recognised as a TOP abstract, among the best in its category.
Co-authors Thomas Padgett, Principal Operational Researcher, and Fryderyk Wilczynski, Operational Researcher, share exclusive insights into the research’s motivation and innovation.
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Estimating the true prevalence and burden of metabolic dysfunction‑associated steatohepatitis (MASH) remains a major challenge. Conventional approaches rely on cross‑sectional data and proxy biomarkers, often producing inconsistent results.
This work introduces a mechanistic, population‑level framework that treats MASH as a dynamic disease system, not a static estimate. It integrates:
- Demographic change (ageing, mortality, migration)
- Disease progression and regression across fibrosis stages
- Competing risks, including hepatocellular carcinoma and cardiovascular disease
These processes are captured in a system of coupled differential equations spanning 8 disease states and 21 age groups, ensuring consistency over time and across populations.
A key innovation is the treatment of NHANES and FIB‑4 data as imperfect observations of an underlying latent disease process, rather than direct measures of prevalence. This embeds noisy biomarker data within a biologically coherent structure.
The model challenges standard practice by linking all disease states through transition dynamics, enforcing longitudinal consistency, and integrating disease burden directly with epidemiology.
Beyond estimation, it supports scenario analysis, policy evaluation, and treatment impact modelling.
In summary, this framework reframes MASH epidemiology as a unified, data‑integrated system, combining biological mechanisms, population dynamics, and real‑world data to produce more coherent and credible estimates of disease prevalence and burden.