Diabetes Genius

T1D, T2D, gestational, prediabetes, MODY & monogenic, neonatal, LADA, secondary & ketosis-prone diabetes, plus insulin resistance, metabolic syndrome, MASLD/MASH and cardio-metabolic disease.

β/α/δ-cell & islet biology, insulin & glucagon signalling, incretins (GLP-1/GIP), hepatic, muscle, adipose & kidney metabolism, mitochondrial/ER/oxidative stress, inflammation, autoimmunity, AGE-RAGE, gut microbiome & epigenetics.

Genomics (GWAS, eQTL/pQTL, Mendelian randomization, PRS), bulk & single-cell/spatial transcriptomics, epigenomics (methylation, ATAC-seq), proteomics, metabolomics/lipidomics and microbiome — connected DNA → RNA → protein → metabolite → cell → phenotype.

Diabetic kidney disease, retinopathy, neuropathy, cardiovascular disease, heart failure, stroke, PAD, diabetic foot & wound healing, cognitive and liver complications — mechanism → biomarkers → targets → therapies → outcomes.

Genetic, human, animal, cellular and multi-omics evidence for diabetes targets — with tractability, causal evidence, existing pharmacology, safety, prior failures and research gaps, clearly separating established from predicted.

Approved, investigational and discontinued drugs, repurposing opportunities and modalities from small molecules and biologics to peptides, RNA, gene, cell therapies and protein degraders — across the full discovery-to-development cascade.

Diagnostic, prognostic, predictive, PD and treatment-response biomarkers across omics, imaging and digital sources — CGM, wearables and companion diagnostics — with performance, validation and clinical-utility context (never invented).

Design, intervention, mechanism, population, endpoints, biomarkers, comparators, results and adverse events — surfacing failed hypotheses, under-studied populations and trial-design opportunities.

Authorized evidence on adverse events, safety signals, recalls, regulatory actions, device failures, adherence, persistence and real-world effectiveness — distinguishing a signal from confirmed causality.

EDA, differential expression, regression, survival, clustering, dimensionality reduction, pathway & network analysis, causal inference, Mendelian randomization, multi-omics integration and predictive modelling on authorized datasets.

Reasons across Disease ↔ Gene ↔ Variant ↔ RNA ↔ Protein ↔ Pathway ↔ Metabolite ↔ Cell ↔ Biomarker ↔ Target ↔ Drug ↔ Trial ↔ Outcome, with every claim traceable to primary literature, datasets or trial IDs.

Finds conflicting findings, unexplained mechanisms, missing biomarkers, poorly-validated targets, translational and reproducibility gaps — then frames testable hypotheses and the experiments to resolve them.