A plethora of imaging and blood-based noninvasive tests have been developed to identify patients at risk for non-alcoholic steatohepatitis (NASH). However, many of these proposed biomarkers are not aetiology-specific and are therefore unable to distinguish at-risk NASH with high precision.
As such, a pressing need for new and precise biomarkers for this patient population remains. Importantly, proteo-transcriptomic analyses have emerged as a particularly promising approach to accurately detect at-risk NASH.
This paper aimed to highlight the main findings of Govaere et al. (2023), who developed a predictive model able to effectively characterise high-risk NASH via staged proteo-transcriptomic analyses.
Govaere et al. (2023)’s composite predictive model is based on four omics-identified circulating protein levels combined with two clinical variables (body mass index and type 2 diabetes mellitus). This model exhibited high diagnostic accuracy for at-risk NASH, possessing a positive predictive value of 79% in both derivation and validation cohorts.
However, its efficacy remains to be tested in ethnically diverse populations. Nonetheless, these findings highlight the translatability of proteomic panels, whose clinical potential warrants their development into blood-based tools to risk-stratify non-alcoholic fatty liver disease patients in a primary care setting.