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

Nakayasu, Ernesto; Bramer, Lisa; Ansong, Charles; Schepmoes, Athena; Fillmore, Thomas; Gritsenko, Marina; Clauss, Therese RW; Gao, Yuqian; Piehowski, Paul; Stanfill, Bryan; Engel, Dave; Orton, Daniel J.; Moore, Ronald J.; Qian, Weijun; Sechi, Salvatore; Frohnert, Brigitte I.; Toppari, Jorma; Ziegler, Anette‐G.; Lernmark, Åke; Hagopian, William; Akolkar, Beena; Smith, Richard; Rewers, Marian; Webb-Robertson, Bobbie-Jo M.; Metz, Thomas O.

doi:10.1016/j.xcrm.2023.101093

Cited by 9 publications

(11 citation statements)

References 52 publications

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“…In terms of predictive biomarkers for T1D, The Environmental Determinants of Diabetes in the Young (TEDDY) proteomics study has also identified and validated complement proteins as biomarker candidates. 32 This pattern also persists after the onset of the disease. 20 The fact that complement proteins have been consistently reported to be reduced during T1D development and even after onset makes these proteins excellent biomarker candidates.…”

Section: Discussionmentioning

confidence: 86%

Decrease in multiple complement proteins associated with development of islet autoimmunity and type 1 diabetes

Webb-Robertson,

Nakayasu,

Dong

et al. 2024

iScience

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Section: Discussionmentioning

confidence: 86%

Decrease in multiple complement proteins associated with development of islet autoimmunity and type 1 diabetes

Webb-Robertson,

Nakayasu,

Dong

et al. 2024

iScience

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“…The 26 biomarker candidates uniquely predicted in the augmented dataset were narrowed down to 20 based on their confirmed detection in the untargeted proteomics analyses in blood samples of the TEDDY and DAISY cohorts [71, 74]. Then, further selection from the various represented protein families was made based on the change in their expression level in the high-risk subjects relative to the healthy controls.…”

Section: Resultsmentioning

confidence: 99%

“…To validate the biological relevance of the biomarker candidates predicted by IPA in the augmented data obtained from the AAb positive high-risk subjects in our small cohort, we performed parallel canonical pathway analyses using independently the original (O-HR) and the augmented proteomics (A3-HR) datasets of the same high-risk subjects as well as the previously obtained proteomics data from the AAb positive progressor (C3) and non-progressor (C4) groups from the TEDDY cohort; also named T2 and I2 respectively in reference [74]. The analyses were performed using IPA ’s Canonical Pathway module with a focus on immune inflammatory functions, as previously described in detail [31].…”

Section: Resultsmentioning

confidence: 99%

“…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).…”

Section: Methodsmentioning

confidence: 99%

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

Alcazar,

Chuang,

Ren

et al. 2024

Preprint

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(1) Background: Biomarkers of early pathogenesis of type 1 diabetes (T1D) are crucial to enable effective prevention measures in at-risk populations before significant damage occurs to their insulin producing beta-cell mass. We recently introduced the concept of integrated parallel multi-omics and employed a novel data augmentation approach which identified promising candidate biomarkers from a small cohort of high-risk T1D subjects. We now validate selected biomarkers to generate a potential composite signature of T1D risk; (2) Methods: Twelve candidate biomarkers, which were identified in the augmented data and selected based on their fold-change relative to healthy controls and cross-reference to proteomics data previously obtained in the expansive TEDDY and DAISY cohorts, were measured in the original samples by ELISA; (3) Results: All 12 biomarkers had established connections with lipid/lipoprotein metabolism, immune function, inflammation, and diabetes, but only 7 were found to be markedly changed in the high-risk subjects compared to the healthy controls: ApoC1 and PON1 were reduced while CETP, CD36, FGFR1, IGHM, PCSK9, SOD1, and VCAM1 were elevated; (4) Conclusions: Results further highlight the promise of our data augmentation approach in unmasking important patterns and pathologically significant features in parallel multi-omics datasets obtained from small sample cohorts to facilitate the identification of promising candidate T1D biomarkers for downstream validation. They also support the potential utility of a composite biomarker signature of T1D risk characterized by the changes in the above markers.

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“…Prediction algorithms using AI approaches for cancer [ 20 , 21 ], CVD [ 22 ] and autoimmunity [ 23 – 25 ] have shown promising results. AI has also been applied in type 1 diabetes, for instance in optimising insulin pump settings [ 26 ], in potentially identifying predictive biomarkers [ 27 ] and for the detection of complications [ 28 ].…”

Section: Using Artificial Intelligence To Drive a Precision Medicine ...mentioning

confidence: 99%

Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data

Teixeira,

Battelino,

Carlsson

et al. 2024

Diabetologia

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The type 1 diabetes community is coalescing around the benefits and advantages of early screening for disease risk. To be accepted by healthcare providers, regulatory authorities and payers, screening programmes need to show that the testing variables allow accurate risk prediction and that individualised risk-informed monitoring plans are established, as well as operational feasibility, cost-effectiveness and acceptance at population level. Artificial intelligence (AI) has the potential to contribute to solving these issues, starting with the identification and stratification of at-risk individuals. ASSET (AI for Sustainable Prevention of Autoimmunity in the Society; www.asset.healthcare) is a public/private consortium that was established to contribute to research around screening for type 1 diabetes and particularly to how AI can drive the implementation of a precision medicine approach to disease prevention. ASSET will additionally focus on issues pertaining to operational implementation of screening. The authors of this article, researchers and clinicians active in the field of type 1 diabetes, met in an open forum to independently debate key issues around screening for type 1 diabetes and to advise ASSET. The potential use of AI in the analysis of longitudinal data from observational cohort studies to inform the design of improved, more individualised screening programmes was also discussed. A key issue was whether AI would allow the research community and industry to capitalise on large publicly available data repositories to design screening programmes that allow the early detection of individuals at high risk and enable clinical evaluation of preventive therapies. Overall, AI has the potential to revolutionise type 1 diabetes screening, in particular to help identify individuals who are at increased risk of disease and aid in the design of appropriate follow-up plans. We hope that this initiative will stimulate further research on this very timely topic. Graphical Abstract

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

Cited by 9 publications

References 52 publications

Decrease in multiple complement proteins associated with development of islet autoimmunity and type 1 diabetes

Decrease in multiple complement proteins associated with development of islet autoimmunity and type 1 diabetes

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

Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data

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