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Reproducibility of Biomarkers in MASLD: A Benchmark for Clinically Meaningful Change in Serially Measured
Tsz Yuen Au1, Amanda Darekar2, Vincent Wai-Sun Wong3
1Translational & Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.
Background:
The use of non-invasive tests NITs to monitor disease severity in metabolic-dysfunction associated steatotic liver disease (MASLD) necessitates a clear understanding of clinically meaningful changes at the patient level. This study assessed the measurement variation of NITs in the pre-randomisation screening pipeline of a Phase 2 clinical trial in patients with biopsy confirmed at-risk MASLD.
Methods:
This study was conducted across 198 centres in 11 countries. The Screening Analysis Set (ScrAS) (n=803) comprised patients screened for trial entry with an evaluable biopsy for eligibility assessment. The Randomised Analysis Set (RAS) (n=255) comprised participants with histologically confirmed at-risk steatohepatitis that fulfilled all trial entry criteria and represents a more highly characterised subset nested within the larger ScrAS dataset. Each NITs were evaluated for measurement variation, with results reported as Within-subject % Coefficient of Variation (wCV) and Reproducibility Coefficient % (RDC).
Results:
The analysis of ScrAS demonstrates that liver stiffness measurement by vibration-controlled transient elastography (LSM-VCTE) exhibits a wCV of 19.9%, with a corresponding RDC of 55.2%. LSM-VCTE in RAS shows a wCV of 23.0% and RDC of 63.8%. While controlled attenuation parameter had the lowest wCV (8.8% in ScrAS; 8.7% in RAS) and RDC (24.2% in ScrAS; 24.1% in RAS) among all FibroScan™ derived parameters, the enhanced liver fibrosis score demonstrated the lowest wCV (3.4%) and RDC (9.3%) across all biomarkers.
Conclusions:
These data provide a clear and robust measure of reproducibility for a range of widely adopted NITs, providing insights into the magnitude of biomarker change that may be biologically meaningful and so informing both longitudinal monitoring of disease progression in clinical practice and assessment of treatment response in therapeutic trials.
Clinicaltrials:
gov (NCT#04321031) IMPACT AND IMPLICATIONS: The lack of disease specific biomarker reproducibility data has hampered the adoption of NITs to longitudinally monitor disease natural history in clinical practice and hindered the regulatory adoption of NITs as tractable surrogate endpoints to support drug development as it leads to uncertainty about what magnitude of biomarker change indicates genuine disease progression. The present study provides valuable insights that address both these challenges by providing a clear and robust measure of reproducibility for a range of widely adopted NITs, which are actionable data facilitating longitudinal monitoring of disease progression and treatment response.
