Plasma protein biomarkers predict both the development of persistent autoantibodies and type 1 diabetes 6 months prior to the onset of autoimmunity: the TEDDY Study

Abstract

Type 1 diabetes (T1D) results from an autoimmune destruction of pancreatic β cells. A significant gap in understanding the disease cause is the lack of predictive biomarkers for each of its developmental stages. Here, we conducted a blinded, two-phase case-control plasma proteomics analysis of children enrolled in the TEDDY study to identify biomarkers predictive of autoimmunity and T1D development. First, we performed untargeted proteomics analyses of 2,252 samples from 184 individuals and identified 376 regulated proteins. Complement/coagulation, inflammatory signaling and metabolic proteins were regulated even prior to autoimmunity onset. Extracellular matrix proteins and antigen presentation were differentially regulated in individuals with autoimmunity who progressed to T1D versus those who maintained normoglycemia. We then performed targeted proteomics measurements of 167 proteins in 6,426 samples from 990 individuals and validated 83 biomarkers. A machine learning analysis predicted both the development of persistent autoantibodies and T1D onset 6 months before autoimmunity initiation, with an area under the receiver operating characteristic curve of 0.871 and 0.918, respectively. Our study identified and validated biomarkers highlighting pathways affected in different stages of T1D development.

Systematic review of type 1 diabetes biomarkers reveals regulation in circulating proteins related to complement, lipid metabolism, and immune response

“…Our literature search identified 6 papers that investigated the temporal protein abundance changes in individuals with T1D. Studies by Moulder et al [8], Fronhert et al [9], Nakayasu et al [10], and Webb-Robertson et al (unpublished) looked at protein abundance changes at both pre- and post-seroconversion stages. In contrast, von Toerne et al and Lui et al examined the protein profile only after seroconversion [11, 12].…”

“…In this context, machine learning can be an excellent approach to identifying multivariate panels of proteins to serve as biomarkers of T1D development. This approach has been used to combine metabolic, genetic, and autoimmune signatures to predict the onset of disease and can be easily adapted to test peptide/protein panels [9, 10, 65, 66]. Another concept that can further improve biomarkers’ robustness is using ratio between protein abundance changes rather than profiling individual proteins.…”

“…Our literature search identified 6 papers that investigated the temporal protein abundance changes in individuals with T1D. Studies by Moulder et al [8], Fronhert et al [9], Nakayasu et al [10]…”

A Composite Biomarker Signature of Type 1 Diabetes Risk Identified via Augmentation of Parallel Multi-Omics Data from a Small Cohort

“…As a first step in the validation process, we selected 12 from the 41 candidate biomarkers that were predicted in the augmented proteomics and metabolomics datasets from the high-risk subjects (Table 1). Selection was by cross-reference to a similar group of 401 subjects in the TEDDY cohort who were seroconverted (AAb positive) but without clinical disease at the time of analysis (i.e., non-progressors) [76] and by other ranking criteria as detailed above. We next performed protein measurements by ELISA in the original plasma samples where the quadra-omics studies and the biomarker prediction were performed based on LC-MS/MS assessments (Figure 4) [31].…”

“…Proteomics analyses were performed in the TEDDY/DAISY cohorts and in the small cohort of samples noted above, as previously described in detail in [74,98] and [31], respectively. In brief, LC-MS/MS analysis in the small cohort was done on a Waters NanoAquity UPLC system with a custom packed C18 column (70 cm × 75 μm i.d., Phenomenex Jupiter, 3 μm particle size, 300 Å pore size) coupled to a Q-Exactive mass spectrometer (Thermo Fisher Scientific).…”