Type 2 diabetes: who works on it

One card per institution. Filter by target, by country, or show only institutions in EU-13 (Horizon Europe widening) countries.

• All targets
• GLP-1 64
• SGLT2 40
• PPAR 0
• AMPK 2
• DPP-4 7
• PI3K 0
• AKT 0
• GIP 4
• mTOR 0
• NRF2 0
• NLRP3 3
• SIRT1 1
• STAT3 0
• TP53 1
• TNF 0
• MAPK 0
• PCSK9 1
• VEGF 2
• JNK 0
• TLR4 0
• FTO 0
• IL-6 2
• ERK 0
• HIF-1 0
• JAK 0
• HMGB1 0
• APOE 0
• BDNF 0
• IL-1 1
• STING 0
• CTNNB1 0
• GPX4 0
• PTEN 0
• IL-17 1
• STK11 0
• MMP-9 1
• BCL-2 0
• COX-2 0
• MEK 0
• PINK1 0
• CD40 0
• EGFR 0
• PD-1 0
• RANKL 0
• YAP 0
• ATM 0
• FGFR 0
• MET 1
• PD-L1 0
• SNCA 0
• CCR5 0
• CDKN2A 0
• CXCR4 0
• IGF1R 0
• MAPT 0
• METTL3 0
• PNPLA3 0
• ABL1 0
• BACE1 0
• CD38 0

EU-13 is a filter, not a ranking: it only narrows the list.

10 of 10+ institutions, ordered by recent publications and open trials. This is not a ranking of quality.

Trials whose registry record says individual participant data (IPD) will be shared.

• Janus Kinase (JAK) Inhibitors to Preserve C-Peptide Production in New Onset Type 1 Diabetes (T1D)

  NCT05743244· National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)· Source

Find publications on this disease by a MeSH method term, for example a sequencing or imaging method.

• Single-Cell Analysis 25
• Organoids 3
• Mendelian Randomization Analysis 228
• CRISPR-Cas Systems 5
• Gene Expression Profiling 112
• Sequence Analysis, RNA 8
• Whole Genome Sequencing 8
• Exome Sequencing 12
• Proteomics 148
• Metabolomics 202
• Mass Spectrometry 42
• Flow Cytometry 19
• Immunohistochemistry 50
• Xenograft Model Antitumor Assays 34
• Positron-Emission Tomography 31
• Magnetic Resonance Imaging 186
• Machine Learning 195
• Deep Learning 32
• Cohort Studies 1108
• Genome-Wide Association Study 313

• Machine Learning Models for Glycemic Prediction in Type 1 and Type 2 Diabetes: Systematic Review and Meta-Analysis.

  J Med Internet Res, Nov 2026

• Serum Metabolic Differences Across Glucose Metabolic States Using Untargeted UPLC-HRMS Metabolomics.

  J Proteome Res, Nov 2026

• Machine learning-based identification of depression in older patients with type 2 diabetes mellitus: A multicenter retrospective study.

  Sci Prog, Nov 2026

• Developing Machine Learning-Based Prognostic Models for Nonmelanoma Skin Cancer Patients With Comorbid Diabetes: A Retrospective Cohort Study.

  Cancer Med, Nov 2026

• Identification of ageing-related genes in prostate cancer with type 2 Diabetes using machine learning: a multi-omics study focusing on SOX4, PRNP, and FOXO4.

  Artif Cells Nanomed Biotechnol, Oct 2026

• Dissecting polycystic metabolic ovarian syndrome heterogeneity (PMOS): multi-omics, AI subtyping, and precision management.

  Mol Biol Rep, Oct 2026

• [Construction of predictive models for type 2 diabetic retinopathy based on the principle of differentiation of syndrome elements].

  Nan Fang Yi Ke Da Xue Xue Bao, Oct 2026

• Machine learning predictors of cardiovascular autonomic neuropathy in type 2 diabetes: insights from a prospectively collected single-center registry.

  Front Endocrinol (Lausanne), Oct 2026

• Risk prediction models for macrovascular complications in type 2 diabetes mellitus: from traditional risk scores to multimodal intelligent prediction.

  Front Endocrinol (Lausanne), Oct 2026

• Integrative Bioinformatics Analysis Incorporating Artificial Intelligence and Machine Learning Identifies Putative Pathogenic HNF1B Missense Variants Associated with HNF1B-MODY.

  Int J Mol Sci, Oct 2026

See which targets of this disease your abstract mentions, and go to the people working on them.