Search PubMedSearch

PubMed · 42106685

Integrative proteogenomic and observational analysis identifies potential biomarkers for latent autoimmune diabetes in adults.

Abstract

BACKGROUND: Latent autoimmune diabetes in adults (LADA) shares core genetic and immunological features with type 1 diabetes (T1D) but is frequently misdiagnosed as type 2 diabetes (T2D). With few biomarkers for its timely diagnosis and management, this study integrated proteome-wide Mendelian randomisation (MR) and observational clinical analysis to identify potential LADA biomarkers. METHODS: We performed proteome-wide MR using cis-protein quantitative trait loci (cis-pQTLs) for 1,389 plasma proteins from the deCODE study (n = 35,559) and genome-wide association study (GWAS) data for LADA (2,634 cases and 5,947 controls, European ancestry). Robustness was enhanced via multiple sensitivity analyses. Pathway enrichment analysis, druggability evaluation, phenome-wide MR, and interaction analyses were performed to investigate the clinical relevance and biological context of candidate proteins. Candidate proteins were further evaluated using enzyme-linked immunosorbent assays in a matched Chinese clinical study (n = 241) to assess their discriminative ability for LADA. RESULTS: Proteome-wide MR and colocalisation analyses indicated associations between genetically predicted plasma levels of C-X-C motif chemokine ligand 10 (CXCL10; OR [95% CI] per 1-SD increase in protein levels: 5.49 [1.74,17.32]), serum amyloid A1 (SAA1; 1.28 [1.14,1.45]), and SAA2 (1.22 [1.11,1.34]) with LADA risk. Replication, multi-tissue eQTL, and multivariable MR supported CXCL10's association. Druggability evaluation suggested CXCL10 as a drug target under investigation, and phenome-wide MR of 1,006 diseases and traits indicated no major safety concerns for CXCL10 as a potential biomarker. In the observational clinical study, CXCL10 differentiated LADA from healthy controls (area under the receiver operating characteristic curve [ROC-AUC]: 0.889; precision-recall area under the curve [PR-AUC]: 0.919) and T2D (ROC-AUC: 0.838; PR-AUC: 0.921), with both models showing adequate calibration. CONCLUSIONS: This study suggests that CXCL10 is a putative biomarker associated with LADA, demonstrating discriminative ability to distinguish LADA from T2D in an observational clinical cohort. These findings contribute to understanding the autoimmune molecular aetiology of LADA and support its diagnostic potential in resolving the clinical ambiguity between LADA and T2D.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yuanqin Yang, Jiawen Lu, Zhenqian Wang, Tianyuan Lu, Jin Ding, Ruofan Wang, Sirui Zhou, Zhiguang Zhou, Jingyi Hu. 2026-05-09. Integrative proteogenomic and observational analysis identifies potential biomarkers for latent autoimmune diabetes in adults.. https://doi.org/10.1186/s12933-026-03203-2

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans