ArticleBiomolecules2021
Parallel Multi-Omics in High-Risk Subjects for the Identification of Integrated Biomarker Signatures of Type 1 Diabetes.
Article in Biomolecules, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed, 30 citations in OpenAlex.
- Article
- Multi-omic analysis of human PHACTR1 signaling networks.Communications biology · 2026Article
- Artificial intelligence-driven therapeutics for disease modification in type 1 diabetes: a digital public health and clinical translation framework.Frontiers in public health · 2026Review
- Metabolomics Reveals Potential Biomarkers for Early Detection of irAEs in ICI-Treated Patients.Drug design, development and therapy · 2026Article
- Research progress and application prospects of multi-omics integration strategies in precision risk stratification of type 1 diabetes mellitus.Frontiers in immunology · 2026Review
- Blood pro-thrombotic analytes and platelet activation are associated with post-acute sequelae of COVID-19.BMC infectious diseases · 2025Observational
- Exploring the hub gene CERS6 as a therapeutic target in type 1 diabetes through a bioinformatics and network analyst approach.Scientific reports · 2025Article
- Bioengineering and omics approaches for Type 1 diabetes practical research: advancements and constraints.Annals of medicine · 2025Review
- Illuminating diabetesWorld journal of diabetes · 2025Review
- Supervised Parametric Learning in the Identification of Composite Biomarker Signatures of Type 1 Diabetes in Integrated Parallel Multi-Omics Datasets.Biomedicines · 2024Article
- Gene expression analysis reveals diabetes-related gene signatures.Human genomics · 2024Article
- Protocol for a nested case-control study design for omics investigations in the Environmental Determinants of Islet Autoimmunity cohort.Annals of medicine · 2023Article
- Integrative multi-omics approaches to explore immune cell functions: Challenges and opportunities.iScience · 2023Review
- Recent Developments in Islet Biology: A Review With Patient Perspectives.Canadian journal of diabetes · 2023Review
- Multi-omics of NET formation and correlations with CNDP1, PSPB, and L-cystine levels in severe and mild COVID-19 infections.Heliyon · 2023Article
- Exploring Computational Data Amplification and Imputation for the Discovery of Type 1 Diabetes (T1D) Biomarkers from Limited Human Datasets.Biomolecules · 2022Article
- Review
- Personalized Immunotherapies for Type 1 Diabetes: Who, What, When, and How?Journal of personalized medicine · 2022Review
- An overview of technologies for MS-based proteomics-centric multi-omics.Expert review of proteomics · 2022Article
- Application of Metabolomics in Various Types of Diabetes.Diabetes, metabolic syndrome and obesity : targets and therapy · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundBiomarkers are crucial for detecting early type-1 diabetes (T1D) and preventing significant β-cell loss before the onset of clinical symptoms. Here, we present proof-of-concept studies to demonstrate the potential for identifying integrated biomarker signature(s) of T1D using parallel multi-omics.
methodsBlood from human subjects at high risk for T1D (and healthy controls;
resultsThe final quadra-omics dataset contained 2292 proteins, 328 miRNAs, 75 metabolites, and 41 lipids that were detected in all samples without exception. Disease/function enrichment analyses consistently indicated increased activation, proliferation, and migration of CD4 T-lymphocytes and macrophages. Integrated molecular network predictions highlighted central involvement and activation of NF-κB, TGF-β, VEGF, arachidonic acid, and arginase, and inhibition of miRNA Let-7a-5p. IPA-predicted candidate biomarkers were used to construct a putative integrated signature containing several miRNAs and metabolite/lipid features in the at-risk subjects.
conclusionsPreliminary parallel quadra-omics provided a comprehensive picture of disturbances in high-risk T1D subjects and highlighted the potential for identifying associated integrated biomarker signatures. With further development and validation in larger cohorts, parallel multi-omics could ultimately facilitate the classification of T1D progressors from non-progressors.
Indexed as
Identifiers
What Socratic 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 Socratic graph.