Background A recent large multicenter study has identified an algorithm, known as Enhanced Liver Fibrosis (ELF), by combining the serum concentrations of amino-terminal propeptide of procollagen type III (PIIINP), tissue inhibitor of matrix metalloproteinase-1 (TIMP-1) and hyaluronic acid (HA). The algorithm has been shown to predict liver related outcomes in patients with chronic liver diseases and recently it has been shown to correlate with several measures of fibrosis in Systemic Sclerosis (SSc).
Objectives The aim of this study was to compare the ability of ELF and its single components in correlating with the different clinical and instrumental variables to determine whether any of the three biomarkers could have a specific role as surrogate outcome measure in SSc.
Methods The serum concentrations of the three biomarkers were analysed in 210 SSc patients employing the same platform used to calculate the ELF score (Siemens, Advia Centaur). All patients were investigated for clinical features, skin and internal organs involvement, HAQ-DI, disease severity and activity. Statistical analysis was performed using GraphPrism software.
Results Correlation coefficients are shown in the table. All three components of ELF showed a significant correlation with mRSS and were higher in patients with flexion contractures (p<0.05). Interestingly, the concentration of HA was the only parameter correlating with age, muscle and heart involvement. The biomarker with better correlation with lung involvement was TIMP-1, which showed a significant correlation with DLCO% predicted value and lung severity and the serum levels were significantly higher in patients with lung fibrosis as assessed by HRCT scan (p<0.0001). All three serum markers correlated with HAQ-DI and both PIIINP and TIMP-1 with the EScSG-AI.
Conclusions Sub-analysis of the single serum markers included in the ELF score algorithm suggests that the different biomarkers may function as surrogate outcome measure of specific organ involvement in SSc. In this regard, longitudinal studies assessing the sensitivity to change over time of the single biomarkers may pave the way to develop specific algorithms tailored to carry the maximum predictive value on specific organ involvement in SSc.
Disclosure of Interest None Declared
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