Inflammatory protein expression patterns in Hashimoto’s thyroiditis: a cross-sectional observational study

Background
The primary aim of this study is to gain insights into the immunopathogenesis of the most common autoimmune thyroid disorder, Hashimoto’s thyroiditis (HT). A secondary aim is to identify reliable protein biomarkers that can aid in the prediction of the presence and progression of HT, thus advancing disease monitoring and therapeutic development.

Methods
This cross-sectional study included 440 individuals − 267 HT patients and 173 healthy controls. HT patients were stratified based on disease severity into two subgroups: euthyroid/levothyroxine-treated (EUTHY-LT4) and hypothyroid (HYPO). Serum levels of 92 inflammation-related proteins were measured using the state-of-the-art Olink proteomics platform. Group comparisons were conducted using ANOVA and post hoc tests to identify differentially expressed proteins. The Boruta machine learning algorithm was applied to identify protein predictors of disease presence and severity.

Results
A total of 42 differentially expressed proteins were identified, revealing eight distinct expression patterns based on their direction of regulation across comparisons. IL-17C and CCL19 were the most upregulated in HT patients, and ST1A1, CASP-8, CXCL1, SIRT2, and LAP TGF-beta-1 were the most downregulated. An increased IL-17C/IL-10 ratio correlated with disease severity. Additionally, machine learning identified 22 protein markers with potential for distinguishing HT patients from controls, and 7 proteins for differentiating EUTHY-LT4 from HYPO patients.

Conclusions
Our findings provide novel insights into the molecular landscape of immune-related protein changes in HT, highlighting a dynamic inflammatory response that varies with disease progression. These findings may contribute to the development of biomarkers for patient stratification, improved disease management, and targeted therapies to prevent or delay hypothyroidism.