Aims/hypothesis: Biomarkers are needed to accurately predict and monitor type 1 diabetes progression during the substantially heterogeneous presymptomatic period of the disease development. To address this concern, we studied temporal changes in the plasma and serum proteomes of < 5-year-old children with HLA-conferred risk for type 1 diabetes, by analysing longitudinal sample series that were collected at regular intervals between birth and diagnosis. Methods: Using mass spectrometry-based discovery proteomics, longitudinal sample series from children positive for multiple autoantibodies who had rapidly progressed to type 1 diabetes before 4 years of age (n=10) were analysed and compared with similar measurements from children, matched from age and gender who were either positive for single autoantibody (n=10) or autoantibody negative (n=10). Following analysis of the data with an additive Gaussian process regression model (LonGP), targeted proteomics was used to verify 11 biomarker candidates in a similar yet independent cohort of children who progressed to the disease within 5 years of age (n=31) and matched autoantibody negative children (n=31). Results: These data reiterated extensive age related trends for protein levels in young children. Further, by combining the utility of LonGP together with the targeted analysis in an extended cohort with more frequent sampling points, these analysis demonstrated that the serum levels of two peptides unique for apolipoprotein C1 (APOC1) were decreased after the appearance of the first islet autoantibody and remained relatively less abundant in children who later progressed to type 1 diabetes, in comparison to autoantibody negative children. Conclusions/interpretation: Through the comparative analysis of prospectively collected samples, our data indicate that the serum/plasma levels of APOC1 are decreased in islet autoantibody positive children who develop type 1 diabetes at a young age. In future studies, APOC1 could be used as an additional measurement to improve early prediction of type 1 diabetes and selection of subjects for intervention studies.