Identification of Progressive Islet Biomarkers for Type 2 Diabetes by Integrated Transcriptomics and Mendelian Randomization: Alterations Spanning the Normal Glucose Tolerance–Impaired Glucose Tolerance–Type 2 Diabetes Continuum

Background: Impaired glucose tolerance (IGT) is the principal prediabetic stage preceding type 2 diabetes mellitus (T2D), yet islet-specific biomarkers capable of tracking progressive molecular changes from normal glucose tolerance (NGT) through IGT to T2D remain unestablished. We sought to identify a multi-gene biomarker panel with monotonically increasing expression and causal support across the full glycemic continuum. Methods: Two human islet transcriptomic datasets (GSE76895, GSE164416; n = 181: NGT 50, IGT 56, T2D 75) were integrated following ComBat batch correction. Candidate biomarkers were identified at the intersection of limma differential expression and weighted gene co-expression network analysis (WGCNA), then refined through a five-algorithm machine learning consensus (LASSO, random forest, XGBoost, SVM-RFE, elastic net). Progressive expression was assessed by the Jonckheere–Terpstra (J–T) trend test. Two-sample Mendelian randomization (MR) with eQTLGen cis-eQTL instruments, Steiger directionality testing, and Bayesian colocalization provided causal inference. A diagnostic model was internally validated via 1000-iteration bootstrap resampling. Cell-type specificity was verified using single-cell RNA sequencing (GSE200044; 127,919 cells). Results: A 12-gene biomarker panel (ALDOB, DKK3, PCOLCE2, KCNE4, INHBA, IRF8, ITGB2, LAPTM5, MYOF, RAMP3, RUNX2, S100A4) was identified, with all members passing Bonferroni-corrected J–T trend testing across the NGT–IGT–T2D axis (p ≤ 2.4 × 10−3). Notably, a direct IGT-versus-NGT transcriptome-wide comparison (11,948 genes) yielded no significant DEGs after FDR correction, indicating that prediabetic islet signals are subtle and detectable only through progressive trend analysis on preselected candidates. Nevertheless, the mean IGT-stage effect size of the 12 hub genes reached 41.9% of the T2D value, with KCNE4 achieving 94.0% (nominal p = 1.75 × 10−4), identifying it as the earliest-altered biomarker. Two-sample MR using whole-blood eQTLs suggested protective effects of genetically proxied MYOF (OR 0.999, FDR = 1.68 × 10−4) and RUNX2 (OR 0.998, FDR = 1.68 × 10−4) on T2D risk, with Steiger testing supporting an expression-to-disease direction (p < 10−36). However, Bayesian colocalization indicated independent causal variants at both loci (PP.H3 > 0.76, PP.H4 < 0.001), substantially weakening the causal interpretation and suggesting that the MR associations may reflect linkage disequilibrium rather than shared causal biology. The panel achieved a bootstrap-corrected AUC of 0.833 (apparent 0.879) with PR-AUC of 0.951. Single-cell validation confirmed upregulation of 7 hub genes in β cells and revealed cell-type-specific patterns invisible in bulk data, including bidirectional INHBA regulation between β and α cells and progressive α-cell proportion expansion (28.97% → 46.41%). Pathway enrichment converged on three mechanistic axes: extracellular matrix remodeling, immune activation, and autoimmune-like responses, with direct enrichment of the type 1 diabetes pathway (hsa04940). Conclusions: This study establishes a 12-gene progressive islet biomarker panel spanning the NGT–IGT–T2D continuum, supported by machine learning robustness, genetic causal evidence, diagnostic modeling, and single-cell biological validation. KCNE4 emerges as a candidate early-warning biomarker for prediabetes, while MYOF and RUNX2 represent causally supported compensatory targets, collectively providing a multilayered foundation for T2D risk stratification and precision intervention. From a clinical perspective, the identification of progressive islet biomarkers at the prediabetic stage provides molecular support for early lifestyle intervention, reinforcing that timely detection and behavioral modification remain the most effective strategies to prevent T2D progression.