ArticleJCI insight2019
A composite immune signature parallels disease progression across T1D subjects.
Article in JCI insight, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 23 citations in OpenAlex.
- Trial
- Age-independent immune subtypes in type 1 diabetes exhibit distinct post-onset progression rates and immunotherapeutic responses.Diabetologia · 2026Article
- Time to reframe the disease staging system for type 1 diabetes.The lancet. Diabetes & endocrinology · 2024Review
- Human immune phenotyping reveals accelerated aging in type 1 diabetes.JCI insight · 2023Article
- Identification of an anergic BND cell-derived activated B cell population (BND2) in young-onset type 1 diabetes patients.The Journal of experimental medicine · 2023Article
- CeDAR: incorporating cell type hierarchy improves cell type-specific differential analyses in bulk omics data.Genome biology · 2023Article
- Guidelines for standardizing T-cell cytometry assays to link biomarkers, mechanisms, and disease outcomes in type 1 diabetes.European journal of immunology · 2022Article
- LncRNA functional annotation with improved false discovery rate achieved by disease associations.Computational and structural biotechnology journal · 2022Article
- An integrated multi-omics analysis of topoisomerase family in pan-cancer: Friend or foe?PloS one · 2022Article
- The many faces of islet antigen-specific CD8 T cells: clues to clinical outcome in type 1 diabetes.Immunology and cell biology · 2021Review
- Parallel Multi-Omics in High-Risk Subjects for the Identification of Integrated Biomarker Signatures of Type 1 Diabetes.Biomolecules · 2021Article
- Endotypes in T1D: B lymphocytes and early onset.Current opinion in endocrinology, diabetes, and obesity · 2020Review
- Sensitive detection of multiple islet autoantibodies in type 1 diabetes using small sample volumes by agglutination-PCR.PloS one · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors at 7 institutions in 3 countries.
Funding
Abstract
At diagnosis, most people with type 1 diabetes (T1D) produce measurable levels of endogenous insulin, but the rate at which insulin secretion declines is heterogeneous. To explain this heterogeneity, we sought to identify a composite signature predictive of insulin secretion, using a collaborative assay evaluation and analysis pipeline that incorporated multiple cellular and serum measures reflecting β cell health and immune system activity. The ability to predict decline in insulin secretion would be useful for patient stratification for clinical trial enrollment or therapeutic selection. Analytes from 12 qualified assays were measured in shared samples from subjects newly diagnosed with T1D. We developed a computational tool (DIFAcTO, Data Integration Flexible to Account for different Types of data and Outcomes) to identify a composite panel associated with decline in insulin secretion over 2 years following diagnosis. DIFAcTO uses multiple filtering steps to reduce data dimensionality, incorporates error estimation techniques including cross-validation and sensitivity analysis, and is flexible to assay type, clinical outcome, and disease setting. Using this novel analytical tool, we identified a panel of immune markers that, in combination, are highly associated with loss of insulin secretion. The methods used here represent a potentially novel process for identifying combined immune signatures that predict outcomes relevant for complex and heterogeneous diseases like T1D.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.