T2D Research

Type 2 Diabetes

Properties2
tagsconcept, type-2-diabetes
aliasesType 2 Diabetes

5 min read

Type 2 Diabetes

Type 2 diabetes is the disease context for the paper. In this vault, pages about T2D should focus on mechanisms and measurements that matter for PBMC immune changes in type 2 diabetes.

Paper-Relevant Angles

  • Chronic inflammation and immune activation in metabolic disease.
  • Gu et al. 2024 provides single-cell PBMC evidence that T2D is associated with inflammatory monocyte states, cytotoxic T-cell expansion, and B-cell differentiation changes.
  • Insulin resistance, hyperglycemia, adiposity, and comorbidity as potential immune modifiers.
  • Medication and disease-duration effects that can confound immune comparisons.
  • Population and ancestry structure when comparing cohorts.
  • Tkachenko et al. 2025 shows that blood transcriptome meta-analysis in T2D can identify disease-associated pathways missed by single studies, but individual-study DEG lists show low concordance across cohorts.
  • Tkachenko et al. 2025 identifies neutrophil effector biology, ERAD, mTOR signaling, oxidative stress, and RNA splicing as cross-study blood transcriptomic themes in T2D.
  • Tkachenko et al. 2024 (medRxiv preprint) provides a primary-data bulk blood RNA-seq (n=18) and PBMC scRNA-seq (n=4) dataset from a Russian cohort, reporting NK cell depletion and increased CD4+ TCM/naive cells in T2D PBMCs, and 146 DEGs in bulk blood with PCA showing disease status does not explain the majority of transcriptomic variance.
  • Tkachenko et al. 2024’s bulk blood transcriptome data (GSE280402) is a subset of the datasets included in the cross-study meta-analysis of Tkachenko et al. 2025, providing a direct link between primary data generation and meta-analysis efforts by the same group.
  • Tkachenko et al. 2024’s NK cell composition finding contrasts with Gu et al. 2024, illustrating that T2D PBMC single-cell findings can vary across studies even when using similar methodology.
  • Li et al. 2025 frames T2D as an immunometabolic disorder in PBMCs, connecting chronic inflammation, altered monocyte and T-cell proportions, T-cell metabolic pathway activity, and inferred T-cell-monocyte communication.
  • Li et al. 2025 suggests T2D immune signatures may be stratified by T-cell metabolic pathway profiles, but these subtypes require external validation before being used as stable clinical categories.
  • Huang et al. 2022 identifies four islet-derived diagnostic biomarkers (SLC2A2, SERPINF1, RASGRP1, CHL1) with combined nomogram AUC of 0.902, using bulk islet RNA-seq and pancreatic scRNA-seq.
  • Huang et al. 2022 links reduced SERPINF1 expression in pancreatic fibroblasts to T2D and identifies NR2F2 as a potential transcription factor regulator — offering a complementary organ-level (islet) perspective to PBMC-based immune profiling.
  • Huang et al. 2022 reports T2D DEGs enriched for JAK-STAT signaling, Ras signaling, and pancreatic secretion pathways in islet tissue — distinct pathway themes from blood/PBMC transcriptome studies.
  • Tang et al. 2026 integrates pancreatic islet scRNA-seq (GSE221156) with LASSO regression to identify four T2DM signature genes (PNLIP, BUB1, CTSB, NAMPT) with diagnostic AUCs of 0.694–0.931 in an independent skeletal muscle cohort (GSE29221).
  • Tang et al. 2026 provides qRT-PCR validation in peripheral blood from a Chinese cohort (15 T2DM, 20 controls), confirming PNLIP, BUB1, CTSB upregulation and NAMPT downregulation — a cross-compartment (islet→blood) biomarker translation approach.
  • Tang et al. 2026 uses CellChat to identify Alpha and Beta cells as signaling hubs in the islet microenvironment, with MK and SPP1 pathways showing complementary expression patterns.
  • Our prior work (Markelova et al. 2025) demonstrated that T2D genetic mechanisms — measured via partitioned polygenic scores — vary significantly across Chechen, Tatar, and Yakut populations within Russia (same cohort as the current project), with Yakuts showing elevated beta-cell dysfunction and compensatory insulin scores, while Chechens and Tatars show higher obesity-related mechanism scores.
  • Our prior work (Markelova et al. 2025) aligns with Yabe et al.’s East Asian vs. European framework: Yakuts (genetically close to East Asians) show beta-cell-dominant T2D mechanisms, while Chechens and Tatars resemble European insulin-resistance patterns — demonstrating that ancestry-specific T2D heterogeneity exists even within a single country.
  • Our prior work (Markelova et al. 2025) used ADMIXTURE and PCA to confirm that self-reported ancestry matched genetically inferred ancestry across all 275 participants, supporting the use of self-reported ancestry when genetically validated.
  • Yabe et al. 2015 establishes that T2D in East Asians is primarily driven by β-cell dysfunction with lower insulin resistance, contrasting with the insulin-resistance-dominant paradigm in European populations — providing a literature framework for ancestry-specific T2D pathophysiology that motivates ancestry-aware PBMC immune analysis. 1
  • Yabe et al. 2015 documents East-Asian-specific T2D genetic loci (KCNQ1, UBE2E2, C2CD4A/B, PTPRD, SRR, SPRY2, CDC123) predominantly implicating β-cell function, consistent with the East Asian T2D β-cell dysfunction paradigm. 1
  • Yabe et al. 2015 shows incretin-based therapies (dipeptidyl peptidase-4 inhibitors (DPP-4i), glucagon-like peptide-1 receptor agonists (GLP-1RA)) have greater efficacy in East Asians, consistent with β-cell dysfunction being the primary driver — but also notes that medication patterns (sulfonylureas (SUs) + DPP-4i in Japan vs. metformin first-line in Europe) differ systematically by population, which may confound ancestry-group immune comparisons if uncontrolled. 1

Linked Topics

Footnotes

  1. extracted from Yabe et al. 2015 ↩ ↩2 ↩3

Graph View

Backlinks