Type 2 Diabetes Mellitus

Complex MONDO:0005148 Pathograph 22 Show in embeddings browser Diabetes Mellitus Metabolic Disease Endocrine Disease

Type 2 diabetes mellitus is a common metabolic disease defined by chronic hyperglycemia arising from the combination of peripheral insulin resistance and progressive pancreatic beta-cell dysfunction. Excess adiposity, physical inactivity, and polygenic susceptibility drive impaired insulin signaling in muscle, liver, and adipose tissue, with compensatory hyperinsulinemia that eventually fails as beta-cell secretory capacity declines. Hepatic glucose overproduction and incretin axis dysfunction further worsen glycemic control, and sustained hyperglycemia leads to microvascular and macrovascular complications such as retinopathy, nephropathy, and peripheral neuropathy.

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7
Pathophys.
10
Phenotypes
1
Hypotheses
2
Gaps
22
Pathograph
4
Genes
6
Medical Actions
4
Datasets
6
Models
6
References
2
Deep Research
C

Comorbidities

Alzheimer Disease <-> Type 2 Diabetes Mellitus
Disease B B_BEFORE_A CURATED
Atopic Dermatitis <-> Type 2 Diabetes Mellitus
Disease B A_BEFORE_B CANDIDATE
Major Depressive Disorder <-> Type 2 Diabetes Mellitus
Disease B BIDIRECTIONAL CURATED
Type 2 Diabetes Mellitus <-> Union of Lichen Simplex Chronicus, Prurigo Nodularis
Disease A A_BEFORE_B CANDIDATE
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Mechanistic Hypotheses

1
Amplification of polygenic T2D risk in adverse metabolic contexts via shared insulin-resistance convergence
pgs_context_amplification EMERGING
Evidence balance 2 support
Polygenic-score-by-context (PGS×C) interactions reported for type 2 diabetes in the UK Biobank appear to reflect amplification rather than context-specific causal variants: the same susceptibility loci (e.g. TCF7L2, PPARG, KCNJ11, SLC30A8) exert systematically larger effects in disease-promoting metabolic contexts. This entry proposes that the amplification arises because polygenic liability and adverse exposures converge on the shared Insulin Resistance node (with Beta Cell Dysfunction as a parallel target), so their joint effect on the liability-threshold scale is super-additive rather than additive. Nagpal & Gibson (Nat Genet 2026, PMID:42443528) highlight the interaction between reduced polyunsaturated fatty acids (low omega-6) and high glucose, which elevates T2D risk increasingly as the PGS rises, and identify sex and sex-adjusted testosterone as further amplifying contexts.
EMERGING hypothesis motivated by population-scale PGS×context analyses (primary source PMID:42443528; general amplification mechanism corroborated by PMID:37228747, which documents testosterone-mediated amplification). The convergence claim (polygenic liability + adverse metabolic context → Insulin Resistance) is a mechanistic interpretation and is not itself established as causal — see the reverse-causation knowledge gap under discussions.
Show evidence (2 references)
PMID:42443528 SUPPORT Computational
"The predominant mechanism for PGS×C is the amplification of genetic effects in adverse contexts, such as low polyunsaturated fatty acids or social determinants of ill health"
Direct source (Nagpal & Gibson 2026): across seven UK Biobank diseases and 75 contexts, amplification of genetic effects in adverse contexts is identified as the predominant mechanism of PGS×context interaction — the mechanism applied in this hypothesis.
PMID:37228747 SUPPORT Computational
"GxSex is pervasive but acts primarily through systematic sex differences in the magnitude of many genetic effects"
Establishes amplification — systematic differences in the magnitude of polygenic effects rather than in the identity of causal variants — as the primary mode of gene-by-sex interaction across physiological traits, and notes that testosterone may mediate this amplification. Cited as general support for amplification as a mode of PGS×context interaction; the paper is not T2D-specific.
?

Discussions and Knowledge Gaps

2
Are the T2D PGS×context interactions driven by adverse exposures causally amplifying genetic risk, or are some "contexts" (notably circulating glucose) actually downstream readouts of incipient disease (reverse causation)?
KNOWLEDGE GAP OPEN t2d_pgsxc_reverse_causation
Attached to
pathophysiology#Insulin Resistance environmental#Sedentary Lifestyle environmental#High-Calorie Diet
Population PGS×context analyses (Nagpal & Gibson 2026, PMID:42443528) are largely unable to establish the causality of specific contexts. For T2D this is especially acute for circulating glucose, a major amplifying "context" in the paper that is also nearly definitional of the disease and therefore substantially downstream of incipient hyperglycemia rather than a purely upstream driver. Behavioural contexts such as reduced physical activity and higher-calorie diet may also be partly responses to early metabolic decline. Distinguishing genuine amplification from reverse causation determines whether the modelled lifestyle interventions would actually reduce risk.
Proposed experiments
Mendelian randomization of exposure-to-T2D direction across PGS strata
t2d_pgsxc_mr_direction
Use bidirectional / multivariable Mendelian randomization to test whether each candidate context (glucose, omega-6 fatty acids, sex-adjusted testosterone, physical activity) causally affects T2D versus being a consequence of subclinical disease, and whether the causal effect estimate scales with polygenic liability as the amplification model predicts.
Decision criterion
A context is retained as a causal amplifier if MR supports exposure-to-disease directionality and the exposure-attributable risk difference increases across increasing PGS strata; it is flagged as a reverse-causation suspect otherwise.
Prospective incident-T2D analysis restricted to pre-diagnosis exposure windows
t2d_pgsxc_prospective_temporal
Restrict exposures to measurements taken well before diagnosis and repeat the PGS×context liability-threshold modelling on incident cases only, to reduce the chance that exposure values (particularly glucose) reflect early disease rather than antecedent risk.
Decision criterion
Amplification is supported if the PGS×context deviation from additivity persists when only pre-diagnosis exposure windows and incident cases are used.
Is beta-cell dedifferentiation and beta-to-alpha-like trajectory switching in type 2 diabetes a reversible driver of beta-cell failure, or mostly a late marker of intrapancreatic adiposity, inflammation, and metabolic stress?
KNOWLEDGE GAP OPEN gap_t2d_beta_cell_dedifferentiation_reversibility
Attached to
pathophysiology#Beta Cell Dysfunction pathophysiology#Insulin Resistance pathophysiology#Incretin Axis Dysfunction
The entry links insulin resistance to beta-cell dysfunction, but recent human-islet work points to a specific beta-cell identity-loss axis involving intrapancreatic adipocytes, immune recruitment, alpha-like trajectories, and SMOC1. Resolving whether this state is causally reversible would determine whether beta-cell recovery should be modeled as a targetable mechanism rather than only as clinical glycemic improvement.
Proposed experiments
Human islet-adipocyte beta-cell dedifferentiation rescue assay
human islet microphysiological perturbation experiment Relation: this experiment is of type this experiment type This experiment is of type human islet microphysiological perturbation experiment.
exp_t2d_islet_adipocyte_beta_dedifferentiation_rescue
Co-culture human pancreatic islets with intrapancreatic adipocytes and autologous immune cells in a microphysiological system; impose glucolipotoxic stress; perturb SMOC1 and incretin signaling; and test whether beta-cell identity, insulin secretion, and beta-to-alpha-like trajectory markers recover when the adipocyte-inflammatory niche is removed or therapeutically modulated.
Model systems
Human islet-adipocyte-immune microphysiological system
Human islet organ-on-chip or perifusion coculture pairing islets with adipocytes and immune cells to model the pancreatic fat-associated microenvironment implicated in beta-cell identity loss.
ORGAN ON CHIP namo:OrganOnChip link
human NCBITaxon:9606 NCBI Taxonomy (NCBITaxon) Relation: this experimental model is built in this organism This experimental model is built in human, annotated with Homo sapiens (NCBITaxon:9606). NCBITaxon:9606 is an organism from the NCBI Taxonomy.
pancreas UBERON:0001264 Uberon multi-species anatomy ontology (UBERON) Relation: this experimental model uses this anatomical location This experimental model uses pancreas (UBERON:0001264). UBERON:0001264 is an anatomical location from the Uberon multi-species anatomy ontology.
pancreatic beta cell CL:0000169 Cell Ontology (CL) Relation: this experimental model uses this cell type This experimental model uses pancreatic beta cell, annotated with type B pancreatic cell (CL:0000169). CL:0000169 is a cell type from the Cell Ontology. pancreatic alpha cell CL:0000171 Cell Ontology (CL) Relation: this experimental model uses this cell type This experimental model uses pancreatic alpha cell, annotated with pancreatic A cell (CL:0000171). CL:0000171 is a cell type from the Cell Ontology. adipocyte CL:0000136 Cell Ontology (CL) Relation: this experimental model uses this cell type This experimental model uses adipocyte (CL:0000136). CL:0000136 is a cell type from the Cell Ontology. T cell CL:0000084 Cell Ontology (CL) Relation: this experimental model uses this cell type This experimental model uses T cell (CL:0000084). CL:0000084 is a cell type from the Cell Ontology.
Perturbations
Pancreatic adipocyte inflammatory niche
pathophysiology#Beta Cell Dysfunction
Adipocyte proximity plus inflammatory immune-cell recruitment used to model intrapancreatic fat-associated beta-cell stress.
inflammatory response GO:0006954 Gene Ontology (GO) Relation: this perturbation acts on this biological process This perturbation acts on inflammatory response (GO:0006954). GO:0006954 is a biological process from the Gene Ontology.
SMOC1 gain and loss of function
pathophysiology#Beta Cell Dysfunction
Beta-cell SMOC1 overexpression and knockdown used to test whether the alpha-cell-associated trajectory gene is sufficient and necessary for beta-cell dedifferentiation.
SMOC1 hgnc:20318 HUGO Gene Nomenclature Committee (hgnc) Relation: this perturbation targets this gene This perturbation targets SMOC1 (hgnc:20318). hgnc:20318 is a gene from the HUGO Gene Nomenclature Committee.
GLP-1/GIP receptor agonist rescue
pathophysiology#Incretin Axis Dysfunction
Incretin receptor agonist exposure used to test whether clinically relevant treatment restores beta-cell function and identity under an adipocyte-inflammatory niche.
targeted therapy NCIT:C93352 NCI Thesaurus (NCIT) Relation: this perturbation applies this clinical intervention This perturbation applies targeted therapy (NCIT:C93352). NCIT:C93352 is a clinical intervention from the NCI Thesaurus.
Readouts
Glucose-stimulated insulin secretion
pathophysiology#Beta Cell Dysfunction
Dynamic insulin secretion normalized to beta-cell abundance.
insulin secretion GO:0030073 Gene Ontology (GO) Relation: this readout reports on this biological process This readout reports on insulin secretion (GO:0030073). GO:0030073 is a biological process from the Gene Ontology.
glucose-stimulated insulin secretion assay Relation: this readout is measured by this assay This readout is measured by glucose-stimulated insulin secretion assay.
Direction: NEGATIVE
Beta-cell identity and alpha-like trajectory markers
pathophysiology#Beta Cell Dysfunction
Single-cell expression of INS, MAFA, INSM1, NPY, ALDH1A3, GCG, and SMOC1 interpreted as mature beta-cell identity versus dedifferentiated or alpha-like trajectory state.
single-cell transcriptomic profiling Relation: this readout is measured by this assay This readout is measured by single-cell transcriptomic profiling. immunofluorescence assay Relation: this readout is measured by this assay This readout is measured by immunofluorescence assay.
Direction: POSITIVE
Adipocyte-associated immune recruitment
pathophysiology#Insulin Resistance
T-cell proximity and inflammatory-cytokine readouts around adipocyte-islet interfaces.
inflammatory response GO:0006954 Gene Ontology (GO) Relation: this readout reports on this biological process This readout reports on inflammatory response (GO:0006954). GO:0006954 is a biological process from the Gene Ontology.
multiplex cytokine profiling Relation: this readout is measured by this assay This readout is measured by multiplex cytokine profiling. spatial transcriptomic profiling Relation: this readout is measured by this assay This readout is measured by spatial transcriptomic profiling.
Direction: POSITIVE
Controls
Islet-only culture
Donor-matched islets cultured without adipocytes or immune cells.
Non-diabetic donor islet-adipocyte coculture
Parallel coculture built from non-diabetic donor material.
Vehicle-treated stressed coculture
Stressed coculture receiving vehicle instead of incretin or SMOC1 perturbation.
Decision criterion
Dedifferentiation is supported as a reversible causal mechanism if adipocyte-inflammatory challenge induces beta-cell identity loss and impaired insulin secretion, and if SMOC1 suppression or incretin rescue restores mature beta-cell markers and insulin secretion before cell-loss dominates.
Would support
pathophysiology#Beta Cell Dysfunction pathophysiology#Incretin Axis Dysfunction
Show evidence (3 references)
PMID:40072535 SUPPORT Human Clinical
"Higher pancreatic fat content was accompanied by increased beta cell dedifferentiation in the individuals with diabetes."
Supports the human association between pancreatic adiposity and beta-cell dedifferentiation that this experiment tries to make causal and reversible.
PMID:40072535 SUPPORT Human Clinical
"the interactions among adipocytes, immune cells and beta cells in the pancreas microenvironment might contribute to beta cell failure and dedifferentiation"
Motivates modeling the islet-adipocyte-immune microenvironment rather than studying isolated beta cells alone.
+ 1 more reference
⚙

Pathophysiology

7
Impaired GLUT4-Mediated Glucose Uptake
Dysregulation of GLUT4 trafficking in adipocytes and skeletal muscle reduces insulin-stimulated glucose uptake. GULP1 facilitates GLUT4 translocation to the plasma membrane by counteracting ACAP1 inhibition of ARF6 activity. Reduced GULP1 activity therefore decreases peripheral glucose disposal and contributes to systemic insulin resistance.
Adipocyte CL:0000136 Cell Ontology (CL) Relation: this pathophysiological event involves this cell type This pathophysiological event involves Adipocyte (CL:0000136). CL:0000136 is a cell type from the Cell Ontology. Skeletal Muscle Cell CL:0000188 Cell Ontology (CL) Relation: this pathophysiological event involves this cell type This pathophysiological event involves Skeletal Muscle Cell, annotated with cell of skeletal muscle (CL:0000188). CL:0000188 is a cell type from the Cell Ontology.
GULP1 hgnc:18649 HUGO Gene Nomenclature Committee (hgnc) Relation: this pathophysiological event involves this gene This pathophysiological event involves GULP1 (hgnc:18649). hgnc:18649 is a gene from the HUGO Gene Nomenclature Committee. ACAP1 hgnc:16467 HUGO Gene Nomenclature Committee (hgnc) Relation: this pathophysiological event involves this gene This pathophysiological event involves ACAP1 (hgnc:16467). hgnc:16467 is a gene from the HUGO Gene Nomenclature Committee. ARF6 hgnc:659 HUGO Gene Nomenclature Committee (hgnc) Relation: this pathophysiological event involves this gene This pathophysiological event involves ARF6 (hgnc:659). hgnc:659 is a gene from the HUGO Gene Nomenclature Committee.
Insulin Receptor Signaling GO:0008286 Gene Ontology (GO) Relation: this pathophysiological event involves this biological process This pathophysiological event involves Insulin Receptor Signaling, annotated with insulin receptor signaling pathway (GO:0008286). GO:0008286 is a biological process from the Gene Ontology. Protein Transport GO:0015031 Gene Ontology (GO) Relation: this pathophysiological event involves this biological process This pathophysiological event involves Protein Transport (GO:0015031). GO:0015031 is a biological process from the Gene Ontology.
Downstream
Insulin Resistance Impaired GLUT4-mediated uptake reduces peripheral glucose disposal and contributes to systemic insulin resistance.
Show evidence (2 references)
PMID:42436120 SUPPORT Model Organism
"GULP1 significantly enhanced glucose uptake in adipocytes and muscle cells by promoting GLUT4 translocation to the plasma membrane. In obese mice, GULP1 overexpression improved insulin sensitivity and glucose tolerance."
Demonstrates GULP1-dependent GLUT4 trafficking and improved insulin sensitivity in metabolic tissues and obese mice.
PMID:42436120 SUPPORT Model Organism
"Mechanistically, GULP1 counteracted ACAP1's inhibition of ARF6 activity, thereby facilitating insulin-stimulated GLUT4 trafficking."
Establishes the GULP1-ACAP1-ARF6 regulatory mechanism.
Insulin Resistance
Peripheral tissues (muscle, liver, adipose) become resistant to insulin action, requiring higher insulin levels to maintain glucose homeostasis. This leads to compensatory hyperinsulinemia and eventually beta cell exhaustion.
Hepatocyte CL:0000182 Cell Ontology (CL) Relation: this pathophysiological event involves this cell type This pathophysiological event involves Hepatocyte (CL:0000182). CL:0000182 is a cell type from the Cell Ontology. Skeletal Muscle Cell CL:0000188 Cell Ontology (CL) Relation: this pathophysiological event involves this cell type This pathophysiological event involves Skeletal Muscle Cell, annotated with cell of skeletal muscle (CL:0000188). CL:0000188 is a cell type from the Cell Ontology. Adipocyte CL:0000136 Cell Ontology (CL) Relation: this pathophysiological event involves this cell type This pathophysiological event involves Adipocyte (CL:0000136). CL:0000136 is a cell type from the Cell Ontology.
PPARG hgnc:9236 HUGO Gene Nomenclature Committee (hgnc) Relation: this pathophysiological event involves this gene This pathophysiological event involves PPARG (hgnc:9236). hgnc:9236 is a gene from the HUGO Gene Nomenclature Committee.
Insulin Signaling GO:0008286 Gene Ontology (GO) Relation: this pathophysiological event involves this biological process This pathophysiological event involves Insulin Signaling, annotated with insulin receptor signaling pathway (GO:0008286). GO:0008286 is a biological process from the Gene Ontology.
Computational models
Topp Beta-Cell Mass / Insulin / Glucose Model KINETIC COPASI Reduced whole-body insulin sensitivity is the model parameter si; lowering si (or PPARG/thiazolidinedione perturbation of si) reproduces the insulin-resistance driver of type 2 diabetes.
Downstream
Beta Cell Dysfunction Sustained insulin resistance increases compensatory beta-cell insulin demand, contributing to eventual beta-cell failure.
Hepatic Glucose Overproduction Hepatic insulin resistance reduces insulin-mediated suppression of liver glucose production.
Show evidence (4 references)
PMID:12231074 SUPPORT
"Insulin resistance is caused by the decreased ability of peripheral target tissues (especially muscle) to respond properly to normal circulating concentrations of insulin."
This establishes that skeletal muscle is a key site of insulin resistance in type 2 diabetes, with impaired response to normal insulin levels.
PMID:12231074 SUPPORT
"These alterations in glucose transport activity are likely the result of dysregulation of intramyocellular fatty acid metabolism, whereby fatty acids cause insulin resistance by activation of a serine kinase cascade, leading to decreased insulin-stimulated insulin receptor substrate (IRS)-1..."
This describes the molecular mechanism of insulin resistance involving fatty acid-induced serine kinase activation that impairs insulin receptor signaling through IRS-1 and PI3K.
PMID:29939616 SUPPORT
"Insulin resistance impairs glucose disposal, resulting in a compensatory increase in beta-cell insulin production and hyperinsulinemia."
This confirms that insulin resistance leads to compensatory hyperinsulinemia as beta cells attempt to overcome impaired glucose disposal in peripheral tissues.
+ 1 more reference
Beta Cell Dysfunction
Progressive loss of pancreatic beta cell function and mass leads to inadequate insulin secretion relative to insulin demand. Beta cell failure is the key determinant of disease progression.
Pancreatic Beta Cell CL:0000169 Cell Ontology (CL) Relation: this pathophysiological event involves this cell type This pathophysiological event involves Pancreatic Beta Cell, annotated with type B pancreatic cell (CL:0000169). CL:0000169 is a cell type from the Cell Ontology.
KCNJ11 hgnc:6257 HUGO Gene Nomenclature Committee (hgnc) Relation: this pathophysiological event involves this gene This pathophysiological event involves KCNJ11 (hgnc:6257). hgnc:6257 is a gene from the HUGO Gene Nomenclature Committee. SLC30A8 hgnc:20303 HUGO Gene Nomenclature Committee (hgnc) Relation: this pathophysiological event involves this gene This pathophysiological event involves SLC30A8 (hgnc:20303). hgnc:20303 is a gene from the HUGO Gene Nomenclature Committee.
Insulin Secretion GO:0030073 Gene Ontology (GO) Relation: this pathophysiological event involves this biological process This pathophysiological event involves Insulin Secretion (GO:0030073). GO:0030073 is a biological process from the Gene Ontology.
Computational models
Topp Beta-Cell Mass / Insulin / Glucose Model KINETIC COPASI Maximal per-beta-cell secretory capacity (sigma) and the beta-cell-mass state variable (B) capture secretory failure and glucotoxic beta-cell-mass loss; TCF7L2/KCNJ11/HNF1A perturbations act here.
Downstream
Hyperglycemia Insufficient beta-cell insulin secretion limits glucose control and drives hyperglycemia.
Show evidence (5 references)
PMID:37035220 SUPPORT
"Insulin resistance and pancreatic β-cell dysfunction are major pathological mechanisms implicated in the development and progression of type 2 diabetes (T2D)."
This establishes beta cell dysfunction as a core pathological mechanism in type 2 diabetes development alongside insulin resistance.
PMID:37035220 SUPPORT
"Predominant markers of inflammation such as C-reactive protein, tumor necrosis factor alpha, and interleukin-1β are consistently associated with β-cell failure in preclinical models and in people with T2D."
This demonstrates that inflammatory markers are associated with beta cell failure, indicating inflammation contributes to beta cell dysfunction.
PMID:37035220 SUPPORT
"Similarly, important markers of oxidative stress, such as increased reactive oxygen species and depleted intracellular antioxidants, are consistent with pancreatic β-cell damage in conditions of T2D."
This confirms that oxidative stress, characterized by increased ROS and depleted antioxidants, contributes to pancreatic beta cell damage in type 2 diabetes.
+ 2 more references
Hepatic Glucose Overproduction
Impaired suppression of hepatic gluconeogenesis leads to elevated fasting glucose levels. The liver fails to respond appropriately to insulin signals.
Hepatocyte CL:0000182 Cell Ontology (CL) Relation: this pathophysiological event involves this cell type This pathophysiological event involves Hepatocyte (CL:0000182). CL:0000182 is a cell type from the Cell Ontology.
Gluconeogenesis GO:0006094 Gene Ontology (GO) Relation: this pathophysiological event involves this biological process This pathophysiological event involves Gluconeogenesis (GO:0006094). GO:0006094 is a biological process from the Gene Ontology.
Computational models
Topp Beta-Cell Mass / Insulin / Glucose Model KINETIC COPASI Insulin-independent hepatic glucose output is the parameter R0, the target reduced by metformin in the simulated treatment scenarios.
Downstream
Hyperglycemia Excess hepatic glucose production contributes directly to impaired glucose homeostasis.
Show evidence (3 references)
PMID:30150719 SUPPORT
"Diabetes is characterized by impaired glucose homeostasis partly due to abnormally elevated hepatic glucose production (HGP)."
This establishes that elevated hepatic glucose production is a key feature of diabetes pathophysiology.
PMID:30150719 SUPPORT
"Metformin exerts its antihyperglycemic action primarily through lowering hepatic glucose production (HGP)."
This confirms that hepatic glucose overproduction is central to diabetes hyperglycemia, as metformin's primary mechanism targets HGP suppression.
PMID:30150719 SUPPORT
"FBP1 catalyzes the irreversible hydrolysis of fructose-1,6-bisphosphate (F-1,6-P2) to fructose-6-phosphate (F6P) and inorganic phosphate (Pi) in the presence of divalent cations. FBP1 is a key rate-controlling enzyme in the gluconeogenic pathway."
This identifies fructose-1,6-bisphosphatase (FBP1) as a key rate-controlling enzyme in hepatic gluconeogenesis, the pathway responsible for glucose overproduction in diabetes.
Mitochondrial Dysfunction and Oxidative Stress
Early-onset mitochondrial dysfunction and pathological reactive oxygen species (ROS) generation occur across multiple metabolic tissues including pancreatic beta cells, skeletal muscle, and adipose tissue. Impaired mitophagy and mitochondrial dynamics contribute to disease progression. Extracellular vesicle-mediated inter-organ miscommunication propagates oxidative damage.
Pancreatic Beta Cell CL:0000169 Cell Ontology (CL) Relation: this pathophysiological event involves this cell type This pathophysiological event involves Pancreatic Beta Cell, annotated with type B pancreatic cell (CL:0000169). CL:0000169 is a cell type from the Cell Ontology. Skeletal Muscle Cell CL:0000188 Cell Ontology (CL) Relation: this pathophysiological event involves this cell type This pathophysiological event involves Skeletal Muscle Cell, annotated with cell of skeletal muscle (CL:0000188). CL:0000188 is a cell type from the Cell Ontology.
Oxidative Stress Response GO:0006979 Gene Ontology (GO) Relation: this pathophysiological event involves this biological process This pathophysiological event involves Oxidative Stress Response, annotated with response to oxidative stress (GO:0006979). GO:0006979 is a biological process from the Gene Ontology. Mitophagy GO:0000422 Gene Ontology (GO) Relation: this pathophysiological event involves this biological process This pathophysiological event involves Mitophagy, annotated with autophagy of mitochondrion (GO:0000422). GO:0000422 is a biological process from the Gene Ontology.
Show evidence (3 references)
PMID:38338783 SUPPORT
"New evidence suggests that T2D-lean individuals experience early β-cell dysfunction without significant IR. Regardless of the primary event (i.e., IR vs. β-cell dysfunction) that contributes to dysglycemia, significant early-onset oxidative damage and mitochondrial dysfunction in multiple..."
This establishes that mitochondrial dysfunction and oxidative damage occur early and may drive T2D progression regardless of whether insulin resistance or beta cell dysfunction is the primary event.
PMID:38338783 SUPPORT
"Physiological oxidative stress promotes inter-tissue communication, while pathological oxidative stress promotes inter-tissue mis-communication, and new evidence suggests that this is mediated via extracellular vesicles (EVs), including mitochondria containing EVs."
This describes the novel mechanism of extracellular vesicle-mediated oxidative stress propagation between tissues in T2D pathogenesis.
PMID:37035220 SUPPORT
"Similarly, important markers of oxidative stress, such as increased reactive oxygen species and depleted intracellular antioxidants, are consistent with pancreatic β-cell damage in conditions of T2D."
This confirms that oxidative stress characterized by increased ROS and depleted antioxidants contributes to beta cell damage.
Incretin Axis Dysfunction
Impaired incretin hormone signaling, particularly blunted glucose-dependent insulinotropic peptide (GIP) action in beta cells. GLP-1 action is relatively preserved. The incretin effect amplifies insulin secretion in response to oral glucose via cAMP-PKA signaling pathways.
Pancreatic Beta Cell CL:0000169 Cell Ontology (CL) Relation: this pathophysiological event involves this cell type This pathophysiological event involves Pancreatic Beta Cell, annotated with type B pancreatic cell (CL:0000169). CL:0000169 is a cell type from the Cell Ontology. Enteroendocrine Cell CL:0000164 Cell Ontology (CL) Relation: this pathophysiological event involves this cell type This pathophysiological event involves Enteroendocrine Cell (CL:0000164). CL:0000164 is a cell type from the Cell Ontology.
TCF7L2 hgnc:11641 HUGO Gene Nomenclature Committee (hgnc) Relation: this pathophysiological event involves this gene This pathophysiological event involves TCF7L2 (hgnc:11641). hgnc:11641 is a gene from the HUGO Gene Nomenclature Committee.
cAMP Signaling GO:0141156 Gene Ontology (GO) Relation: this pathophysiological event involves this biological process This pathophysiological event involves cAMP Signaling, annotated with cAMP/PKA signal transduction (GO:0141156). GO:0141156 is a biological process from the Gene Ontology. Insulin Secretion Regulation GO:0050796 Gene Ontology (GO) Relation: this pathophysiological event involves this biological process This pathophysiological event involves Insulin Secretion Regulation, annotated with regulation of insulin secretion (GO:0050796). GO:0050796 is a biological process from the Gene Ontology.
Downstream
Beta Cell Dysfunction Reduced beta-cell sensitivity to incretins impairs oral-glucose-stimulated insulin secretion.
Show evidence (3 references)
PMID:38831203 SUPPORT
"Dual glucagon like peptide 1 (GLP1) and glucose-dependent insulinotropic peptide (GIP) receptor agonists are among the new pharmacological strategies recently developed to address this challenge."
This establishes the importance of the GLP-1/GIP incretin axis in T2D pathophysiology and its targeting by dual agonist therapies.
PMID:38831203 SUPPORT
"Tirzepatide, characterized by its ability to selectively bind and activate receptors for the intestinal hormones GIP and GLP-1, has been tested in numerous clinical studies and is already currently authorized in several countries for the treatment of type 2 diabetes and obesity."
This demonstrates the clinical relevance of incretin axis dysfunction by showing dual GLP-1/GIP agonism is effective for T2D treatment.
PMID:19934000 SUPPORT Human Clinical
"The TCF7L2 variant rs7903146 appears to affect risk of type 2 diabetes, at least in part, by modifying the effect of incretins on insulin secretion."
Supports TCF7L2 as a genetic contributor to incretin-axis effects on insulin secretion in humans.
Chronic Hyperglycemia
Sustained hyperglycemia arising from the combined insulin resistance, beta-cell secretory failure, and hepatic glucose overproduction is the shared driver of long-term diabetic tissue injury and the entry point to the conserved diabetic vascular-complication cascade captured by the diabetic_vascular_complications module (chronic hyperglycemia -> oxidative and AGE-RAGE stress -> endothelial dysfunction and vascular inflammation -> micro- and macrovascular injury -> end-organ complications). In type 2 diabetes this cascade manifests clinically as diabetic retinopathy, neuropathy, nephropathy, and atherosclerotic cardiovascular disease.
glucose homeostasis GO:0042593 Gene Ontology (GO) Relation: this pathophysiological event involves this biological process This pathophysiological event involves abnormal glucose homeostasis (GO:0042593). GO:0042593 is a biological process from the Gene Ontology. ⚠ ABNORMAL
Downstream
Diabetic Retinopathy Sustained hyperglycemia drives the microvascular injury that produces diabetic retinopathy, one of the shared end-organ complications captured by the diabetic_vascular_complications module this node conforms to.
Peripheral Neuropathy Sustained hyperglycemia drives the microvascular and metabolic nerve injury that produces diabetic peripheral neuropathy, another shared end-organ complication of the conserved vascular-complication cascade.
Show evidence (1 reference)
PMID:29939616 SUPPORT Other
"This vicious cycle continues until pancreatic beta-cell activity can no longer adequately meet the insulin demand created by insulin resistance, resulting in hyperglycemia."
Supports sustained hyperglycemia arising from beta-cell failure to compensate for insulin resistance, the shared trigger that feeds the diabetic vascular-complication cascade.
⬡

Pathograph

Use the checkboxes to hide or show graph categories. Hover nodes for evidence and cross-linked metadata.
Pathograph: causal mechanism network for Type 2 Diabetes Mellitus Interactive directed graph showing how pathophysiology mechanisms, phenotypes, genetic factors and variants, experimental models, environmental triggers, and treatments relate through causal and linked edges.
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Phenotypes

10
Endocrine 1
Hyperinsulinemia FREQUENT HP:0000842 Human Phenotype Ontology (HP) Relation: this clinical feature is this phenotype This clinical feature is Hyperinsulinemia (HP:0000842). HP:0000842 is a phenotype from the Human Phenotype Ontology.
Compensatory response to insulin resistance
Show evidence (1 reference)
PMID:29939616 SUPPORT
"Insulin resistance impairs glucose disposal, resulting in a compensatory increase in beta-cell insulin production and hyperinsulinemia."
This establishes hyperinsulinemia as a compensatory response to insulin resistance in T2D.
Eye 1
Diabetic Retinopathy OCCASIONAL HP:0000488 Human Phenotype Ontology (HP) Relation: this clinical feature is this phenotype This clinical feature is Retinopathy (HP:0000488). HP:0000488 is a phenotype from the Human Phenotype Ontology.
Long-term microvascular complication
Genitourinary 1
Polyuria FREQUENT HP:0000103 Human Phenotype Ontology (HP) Relation: this clinical feature is this phenotype This clinical feature is Polyuria (HP:0000103). HP:0000103 is a phenotype from the Human Phenotype Ontology.
Show evidence (1 reference)
PMID:9398128 SUPPORT
"Polyuria due to a glucose-induced osmotic diuresis is common in patients with hyperglycemia. This diuresis usually abates when the plasma glucose level approaches its renal threshold."
This describes the mechanism of polyuria in diabetes as glucose-induced osmotic diuresis when plasma glucose exceeds the renal threshold.
Metabolism 3
Hyperglycemia VERY_FREQUENT HP:0003074 Human Phenotype Ontology (HP) Relation: this clinical feature is this phenotype This clinical feature is Hyperglycemia (HP:0003074). HP:0003074 is a phenotype from the Human Phenotype Ontology.
Show evidence (2 references)
PMID:29939616 SUPPORT
"This vicious cycle continues until pancreatic beta-cell activity can no longer adequately meet the insulin demand created by insulin resistance, resulting in hyperglycemia."
This describes how the failure of beta cells to compensate for insulin resistance results in hyperglycemia, the hallmark of type 2 diabetes.
PMID:30150719 SUPPORT
"Diabetes is characterized by impaired glucose homeostasis partly due to abnormally elevated hepatic glucose production (HGP)."
This confirms that hyperglycemia in diabetes results from elevated hepatic glucose production and impaired glucose homeostasis.
Insulin Resistance VERY_FREQUENT HP:0000855 Human Phenotype Ontology (HP) Relation: this clinical feature is this phenotype This clinical feature is Insulin Resistance (HP:0000855). HP:0000855 is a phenotype from the Human Phenotype Ontology.
Show evidence (1 reference)
PMID:12231074 SUPPORT
"Insulin resistance is caused by the decreased ability of peripheral target tissues (especially muscle) to respond properly to normal circulating concentrations of insulin."
This establishes insulin resistance as a hallmark feature of T2D pathophysiology.
Impaired Glucose Tolerance VERY_FREQUENT Glucose intolerance HP:0001952 Human Phenotype Ontology (HP) Relation: this clinical feature is this phenotype This clinical feature is Impaired Glucose Tolerance, annotated with Glucose intolerance (HP:0001952). HP:0001952 is a phenotype from the Human Phenotype Ontology.
Nervous System 2
Polydipsia FREQUENT HP:0001959 Human Phenotype Ontology (HP) Relation: this clinical feature is this phenotype This clinical feature is Polydipsia (HP:0001959). HP:0001959 is a phenotype from the Human Phenotype Ontology.
Show evidence (1 reference)
PMID:9398128 SUPPORT
"Polyuria due to a glucose-induced osmotic diuresis is common in patients with hyperglycemia."
This establishes that polyuria results from glucose-induced osmotic diuresis in hyperglycemia, which in turn leads to polydipsia as a compensatory response to fluid loss.
Peripheral Neuropathy OCCASIONAL HP:0009830 Human Phenotype Ontology (HP) Relation: this clinical feature is this phenotype This clinical feature is Peripheral Neuropathy (HP:0009830). HP:0009830 is a phenotype from the Human Phenotype Ontology.
Long-term microvascular complication
Constitutional 1
Fatigue FREQUENT HP:0012378 Human Phenotype Ontology (HP) Relation: this clinical feature is this phenotype This clinical feature is Fatigue (HP:0012378). HP:0012378 is a phenotype from the Human Phenotype Ontology.
Growth 1
Obesity FREQUENT HP:0001513 Human Phenotype Ontology (HP) Relation: this clinical feature is this phenotype This clinical feature is Obesity (HP:0001513). HP:0001513 is a phenotype from the Human Phenotype Ontology.
🧬

Genetic Associations

4
TCF7L2 (Risk Factor)
Gene: TCF7L2 hgnc:11641 HUGO Gene Nomenclature Committee (hgnc) Relation: this disease-associated gene is this gene This disease-associated gene is TCF7L2 (hgnc:11641). hgnc:11641 is a gene from the HUGO Gene Nomenclature Committee.
PPARG (Risk Factor)
Gene: PPARG hgnc:9236 HUGO Gene Nomenclature Committee (hgnc) Relation: this disease-associated gene is this gene This disease-associated gene is PPARG (hgnc:9236). hgnc:9236 is a gene from the HUGO Gene Nomenclature Committee.
KCNJ11 (Risk Factor)
Gene: KCNJ11 hgnc:6257 HUGO Gene Nomenclature Committee (hgnc) Relation: this disease-associated gene is this gene This disease-associated gene is KCNJ11 (hgnc:6257). hgnc:6257 is a gene from the HUGO Gene Nomenclature Committee.
SLC30A8 (Risk Factor)
Gene: SLC30A8 hgnc:20303 HUGO Gene Nomenclature Committee (hgnc) Relation: this disease-associated gene is this gene This disease-associated gene is SLC30A8 (hgnc:20303). hgnc:20303 is a gene from the HUGO Gene Nomenclature Committee.
💊

Medical Actions

6
Metformin
Action: targeted therapyNCI Thesaurus (NCIT) Relation: this treatment is this clinical intervention This treatment is targeted therapy (NCIT:C93352). NCIT:C93352 is a clinical intervention from the NCI Thesaurus. Ontology label: Targeted Therapy NCIT:C93352
Agent: metformin CHEBI:6801 Chemical Entities of Biological Interest (CHEBI) Relation: this treatment uses this therapeutic agent This treatment uses metformin (CHEBI:6801). CHEBI:6801 is a therapeutic agent from Chemical Entities of Biological Interest.
First-line oral medication that reduces hepatic glucose production and improves insulin sensitivity.
Mechanism Target:
INHIBITS Hepatic Glucose Overproduction — Metformin's primary action is suppression of excessive hepatic gluconeogenesis, so it targets the hepatic-glucose-overproduction node directly (with a secondary insulin-sensitizing effect), lowering fasting glucose upstream of chronic hyperglycemia.
Lifestyle Modification
Action: Lifestyle TherapyNCI Thesaurus (NCIT) Relation: this treatment is this clinical intervention This treatment is Lifestyle Therapy (NCIT:C15900). NCIT:C15900 is a clinical intervention from the NCI Thesaurus. NCIT:C15900
Diet and exercise interventions to reduce weight and improve metabolic health.
Mechanism Target:
INHIBITS Insulin Resistance — Weight loss and exercise improve peripheral insulin sensitivity, acting on the insulin-resistance node that is the primary upstream driver of type 2 diabetes rather than merely lowering glucose downstream.
GLP-1 Receptor Agonists
Action: targeted therapyNCI Thesaurus (NCIT) Relation: this treatment is this clinical intervention This treatment is targeted therapy (NCIT:C93352). NCIT:C93352 is a clinical intervention from the NCI Thesaurus. Ontology label: Targeted Therapy NCIT:C93352
Injectable medications that enhance insulin secretion and promote weight loss.
Mechanism Target:
INHIBITS Incretin Axis Dysfunction — GLP-1 receptor agonists restore the deficient incretin signal, correcting the incretin-axis-dysfunction node to enhance glucose-dependent insulin secretion (with weight loss and appetite effects), upstream of chronic hyperglycemia.
Show evidence (1 reference)
PMID:38831203 SUPPORT
"Dual glucagon like peptide 1 (GLP1) and glucose-dependent insulinotropic peptide (GIP) receptor agonists are among the new pharmacological strategies recently developed to address this challenge."
This establishes GLP-1/GIP receptor agonists as effective pharmacological strategies for T2D treatment.
SGLT2 Inhibitors
Action: PharmacotherapyNCI Thesaurus (NCIT) Relation: this treatment is this clinical intervention This treatment is Pharmacotherapy (NCIT:C15986). NCIT:C15986 is a clinical intervention from the NCI Thesaurus. NCIT:C15986
Oral medications that increase urinary glucose excretion.
Mechanism Target:
INHIBITS Chronic Hyperglycemia — SGLT2 inhibitors act insulin-independently, increasing urinary glucose excretion to lower chronic hyperglycemia directly (with additional cardiorenal protection), targeting the shared hyperglycemia node that feeds the diabetic vascular-complication cascade.
Insulin Therapy
Action: insulin therapyNCI Thesaurus (NCIT) Relation: this treatment is this clinical intervention This treatment is insulin therapy, annotated with Injected Insulin Diabetes Therapy (NCIT:C179441). NCIT:C179441 is a clinical intervention from the NCI Thesaurus. Ontology label: Injected Insulin Diabetes Therapy NCIT:C179441
Required when beta cell function declines significantly.
Mechanism Target:
INHIBITS Chronic Hyperglycemia — When beta-cell secretory capacity has declined enough that endogenous insulin is inadequate, exogenous insulin replaces the shortfall and lowers chronic hyperglycemia directly — a later-line glucose-node intervention once upstream insulin-sensitizing and incretin approaches no longer suffice.
GLP-1/GIP Dual Agonists
Action: targeted therapyNCI Thesaurus (NCIT) Relation: this treatment is this clinical intervention This treatment is targeted therapy (NCIT:C93352). NCIT:C93352 is a clinical intervention from the NCI Thesaurus. Ontology label: Targeted Therapy NCIT:C93352
Tirzepatide and similar agents that activate both GLP-1 and GIP receptors for enhanced glycemic control and weight loss.
Mechanism Target:
INHIBITS Incretin Axis Dysfunction — Tirzepatide co-activates the GIP and GLP-1 receptors, correcting the incretin-axis-dysfunction node more completely than single GLP-1 agonism for enhanced glucose-dependent insulin secretion and weight loss.
Show evidence (1 reference)
PMID:38831203 SUPPORT
"Tirzepatide, characterized by its ability to selectively bind and activate receptors for the intestinal hormones GIP and GLP-1, has been tested in numerous clinical studies and is already currently authorized in several countries for the treatment of type 2 diabetes and obesity."
This confirms tirzepatide as an authorized dual GLP-1/GIP agonist for T2D and obesity treatment.
🌍

Environmental Factors

3
Sedentary Lifestyle
sedentary lifestyle ECTO:6000004 Environmental Conditions, Treatments and Exposures Ontology (ECTO) Relation: this environmental factor is this exposure This environmental factor is sedentary lifestyle, annotated with exposure to sedentary lifestyle (ECTO:6000004). ECTO:6000004 is an exposure from the Environmental Conditions, Treatments and Exposures Ontology.
Major modifiable risk factor
Show evidence (1 reference)
PMID:22890825 SUPPORT Human Clinical
"The greatest sedentary time compared with the lowest was associated with a 112% increase in the RR of diabetes"
Systematic review and meta-analysis quantifies sedentary time's association with type 2 diabetes risk.
High-Calorie Diet
high-calorie dietary pattern XCO:0000013 Experimental Conditions Ontology (XCO) Relation: this environmental factor is this exposure This environmental factor is increased high-calorie dietary pattern, annotated with diet (XCO:0000013). XCO:0000013 is an exposure from the Experimental Conditions Ontology.
Contributes to obesity and insulin resistance
Show evidence (1 reference)
PMID:28065634 SUPPORT Human Clinical
"Risk of diabetes developing was 24% greater for women in the highest dietary energy density quintile compared with the lowest after adjusting for confounders (95% CI 1.17 to 1.32)"
Prospective cohort study (Women's Health Initiative) links higher dietary energy density to increased type 2 diabetes incidence.
Obesity
Primary risk factor for insulin resistance
Show evidence (1 reference)
PMID:20493574 SUPPORT Human Clinical
"The overall RR of diabetes for obese persons compared to those with normal weight was 7.19, 95% CI: 5.74, 9.00 and for overweight was 2.99, 95% CI: 2.42, 3.72"
Meta-analysis of prospective cohort studies quantifies obesity's association with type 2 diabetes risk.
🔬

Biochemical Markers

2
Myeloid Lineage Plasma Proteomic Age Gap (Elevated)
Context: Blood-based cell-type-specific aging clock. Plasma proteins are mapped to their putative cell of origin using Human Protein Atlas single-cell transcriptomic data, and a machine-learning model estimates myeloid lineage biological age; the age gap is the difference between that estimate and chronological age. "Extreme" agers are those in the upper tail of the age-gap distribution.
Pathograph Readouts
Predicts Beta Cell Dysfunction Positive Prognostic
An elevated myeloid lineage age gap predicts incident type 2 diabetes in still-normoglycemic individuals, and is read against beta cell dysfunction via the proposed route of myeloid-derived cytokines inflaming the islet microenvironment. The prognostic association is measured; the islet route is inference and should not be curated as an established causal edge.
Show evidence (1 reference)
PMID:42297981 SUPPORT Human Clinical
"consistent with a role of myeloid-derived cytokines in the initiation of an inflamed pancreatic islet microenvironment and increased susceptibility to type 2 diabetes"
The authors' stated mechanistic rationale linking the myeloid aging signature to the islet compartment, offered as consistency rather than as demonstrated causation.
Show evidence (2 references)
PMID:42297981 SUPPORT Human Clinical
"myeloid lineage extreme aging demonstrated the strongest prognostic value"
Establishes myeloid lineage aging as the leading cellular-aging predictor of incident type 2 diabetes among the cell types tested.
PMID:42297981 SUPPORT Human Clinical
"myeloid lineage aging identified normoglycemic individuals at higher type 2 diabetes risk"
States the clinically distinctive claim: risk stratification before glycemic criteria are met, which is what would make the marker useful for targeted surveillance.
NMR metabolomic risk score (MetRS)
Context: In the UK Biobank metabolome-phenome atlas, MetRS classified prevalent type 2 diabetes with an area under the curve of 0.941 and predicted incident diabetic complications, including diabetic maculopathy (0.921), diabetic kidney failure (0.919) and type 2 diabetes with peripheral circulatory complications (0.913). No presence value is recorded because MetRS is a derived multi-analyte score rather than a measured analyte. Two limits matter for interpretation: the score is trained and tested in a single, predominantly European-ancestry volunteer cohort, and a high classification area under the curve for prevalent disease partly reflects the metabolic consequences of established diabetes rather than antecedent risk.
Pathograph Readouts
Correlates With Chronic Hyperglycemia Positive Diagnostic
The lipid, amino-acid and glycolysis-related measures that dominate MetRS shift with sustained hyperglycemia and insulin resistance, so the score reads out the systemic metabolic state rather than any single mechanism.
Show evidence (3 references)
PMID:40973818 SUPPORT Computational
"MetRS offered favourable diagnostic and predictive performance, particularly in T2D"
Names type 2 diabetes as one of the conditions in which the machine-learning metabolomic risk score performed best, both for classification of prevalent disease and for prediction of incident disease.
PMID:40973818 SUPPORT Computational
"The MetRS witnessed excellent diagnosis for type 1 diabetes (T1D; AUC = 0.944), T2D (AUC = 0.941), diabetic maculopathy (AUC = 0.940) and chronic kidney disease (CKD; AUC = 0.933)."
Quotes the prevalent-disease classification figure of 0.941 for type 2 diabetes that the context field reports.
PMID:40973818 SUPPORT Computational
"the MetRS excellently predicted future diabetic complications, for example, diabetic maculopathy (AUC = 0.921 (95% CI, 0.914-0.928)), diabetic kidney failure (AUC = 0.919 (0.906-0.930)) and type 2 diabetes (T2D) with peripheral circulatory complications (AUC = 0.913 (0.898-0.926))."
Quotes all three incident-complication figures reported in the context field, so the predictive claim and its numbers both rest on source text.
📊

Related Datasets

4
Metformin treatment effects on gut microbiome in T2D bioproject:PRJNA361402
Shotgun metagenomics from 40 individuals in a randomized, placebo-controlled, double-blind type 2 diabetes study. Samples at baseline and after 4 months of metformin treatment to assess drug-microbiome interactions.
human gut metagenome WGS n=40
fecal sample UBERON:0001988 Uberon multi-species anatomy ontology (UBERON) Relation: this dataset samples this sample type This dataset samples fecal sample, annotated with feces (UBERON:0001988). UBERON:0001988 is a sample type from the Uberon multi-species anatomy ontology.
Conditions: type 2 diabetes metformin treatment type 2 diabetes placebo
Nature Communications 2022 - metformin-microbiome interactions
Gut microbiome in urban African type 2 diabetes bioproject:PRJNA607849
16S rRNA gene sequencing of gut microbiome profiles from type 2 diabetes patients and controls in urban African populations, examining geographic and dietary influences on diabetes-associated microbiome signatures.
human gut metagenome
fecal sample UBERON:0001988 Uberon multi-species anatomy ontology (UBERON) Relation: this dataset samples this sample type This dataset samples fecal sample, annotated with feces (UBERON:0001988). UBERON:0001988 is a sample type from the Uberon multi-species anatomy ontology.
Conditions: type 2 diabetes healthy controls
Frontiers Cellular Infection Microbiology 2020
Gut microbiota in obese T2DM patients - Pakistani cohort bioproject:PRJNA554535
16S rRNA sequencing of gut microbiota from 60 Pakistani adults comparing obese individuals with type 2 diabetes to healthy controls. V3-V4 hypervariable regions sequenced.
human gut metagenome n=60
fecal sample UBERON:0001988 Uberon multi-species anatomy ontology (UBERON) Relation: this dataset samples this sample type This dataset samples fecal sample, annotated with feces (UBERON:0001988). UBERON:0001988 is a sample type from the Uberon multi-species anatomy ontology.
Conditions: obese type 2 diabetes healthy controls
PMID:31809500
Chinese MGWAS of gut microbiome in type 2 diabetes bioproject:PRJNA422434
Landmark metagenome-wide association study (MGWAS) comparing gut microbial DNA from 345 Chinese individuals. Identified ~60,000 T2D-associated markers and established metagenomic linkage groups.
human gut metagenome WGS n=345
fecal sample UBERON:0001988 Uberon multi-species anatomy ontology (UBERON) Relation: this dataset samples this sample type This dataset samples fecal sample, annotated with feces (UBERON:0001988). UBERON:0001988 is a sample type from the Uberon multi-species anatomy ontology.
Conditions: type 2 diabetes healthy controls
PMID:23023125
Nature 2012 - first MGWAS of T2D, foundational study
🧮

Computational Models

6
Topp Beta-Cell Mass / Insulin / Glucose Model SBML COPASI KINETIC
Minimal three-ODE dynamical model of the glucose regulatory system (Topp et al. 2000): plasma glucose, plasma insulin, and beta-cell mass, with fast glucose/insulin dynamics on a slow beta-cell-mass manifold. For normal parameters the system is bistable - a physiological steady state (euglycemia) and a pathological, insulinopenic steady state (beta-cell-mass collapse and severe hyperglycemia) separated by a saddle. This makes it the reference dynamical substrate for type 2 diabetes: reduced insulin sensitivity, impaired secretion, or hepatic glucose overproduction decompensates the at-risk state to overt diabetes, and it is wired for perturbation analysis (see the model config sidecar) so that both risk genes and glucose-lowering treatments can be simulated as parameter changes.
Repository ↗ PMID:11013117
Variable Model ID Unit Ontology Mappings Phenotype Thresholds
Fasting_Plasma_Glucose
Plasma glucose concentration at steady state.
 G mg/dL glucose
Hyperglycemia above 126
mild 126 moderate 200 severe 400
Plasma_Insulin
Plasma insulin concentration at steady state. Elevated in the compensated insulin-resistant regime and near-zero after glucotoxic beta-cell-mass collapse.
 I uU/mL
Hyperinsulinemia above 18
mild 18 moderate 30 severe 60
Beta_Cell_Mass
Functional beta-cell mass (slow state variable). Expands under mild hyperglycemia (compensation) and collapses toward zero under sustained extreme hyperglycemia (glucotoxicity), the positive-feedback route to insulinopenic diabetes.
 B mg
Simulation results — 15 scenarios run with dismech-perturb (tellurium / libRoadRunner CVODE) over 4000 h; baseline si = 0.72
Interpretation caveat. This model is bistable and its baseline sits near the saddle separating the physiological and pathological fixed points, so every impairing lesion collapses to the same attractor (glucose 600 mg/dL, beta-cell mass 0) regardless of magnitude. Scenario outcomes are therefore NOT comparable in severity, and severity tiers are suppressed for this model. Two scenarios in particular must not be read as clinical claims: GCK loss-of-function is GCK-MODY (MODY2), which causes mild, non-progressive fasting hyperglycemia with no beta-cell mass loss and usually needs no pharmacotherapy; and the TCF7L2 risk allele is a modest susceptibility factor (per-allele odds ratio ~1.4), not a deterministic route to beta-cell failure.
Scenario Beta_Cell_Mass (mg) Fasting_Plasma_Glucose (mg/dL) Plasma_Insulin (uU/mL) Activated phenotypes
Healthy baseline
si = 0.72
 300.0 100.0 10.0 reference
Severe insulin resistance (si=0.30)
drives Insulin Resistance
0.0
0.00× baseline
 600.0
6.00× baseline
 0.0
0.00× baseline
 Hyperglycemia
Insulin resistance (si=0.45)
drives Insulin Resistance
0.0
0.00× baseline
 600.0
6.00× baseline
 0.0
0.00× baseline
 Hyperglycemia
PPARG loss-of-function (insulin resistance)
drives Insulin Resistance
0.0
0.00× baseline
 600.0
6.00× baseline
 0.0
0.00× baseline
 Hyperglycemia
TCF7L2 risk variant (impaired secretion)
drives Beta Cell Dysfunction
0.0
0.00× baseline
 600.0
6.00× baseline
 0.0
0.00× baseline
 Hyperglycemia
KCNJ11 K_ATP gain (impaired secretion)
drives Beta Cell Dysfunction
0.0
0.00× baseline
 600.0
6.00× baseline
 0.0
0.00× baseline
 Hyperglycemia
HNF1A loss-of-function (MODY3)
drives Beta Cell Dysfunction
0.0
0.00× baseline
 600.0
6.00× baseline
 0.0
0.00× baseline
 Hyperglycemia
GCK loss-of-function (glucose-sensing defect)
drives Beta Cell Dysfunction
0.0
0.00× baseline
 600.0
6.00× baseline
 0.0
0.00× baseline
 Hyperglycemia
SLC5A2 loss-of-function (renal glucosuria; protective)
drives Hyperglycemia
210.0
0.70× baseline
 100.0
1.00× baseline
 7.0
0.70× baseline
 none
Metformin (reduced hepatic glucose output)
drives Hepatic Glucose Overproduction
307.2
1.02× baseline
 100.0
1.00× baseline
 10.24
1.02× baseline
 none
Thiazolidinedione (insulin sensitizer)
drives Insulin Resistance
300.0
1.00× baseline
 100.0
1.00× baseline
 10.0
1.00× baseline
 none
SGLT2 inhibitor (insulin-independent glucose clearance)
drives Hyperglycemia
504.0
1.68× baseline
 100.0
1.00× baseline
 16.8
1.68× baseline
 none
Sulfonylurea (secretagogue) - fails once beta cells collapse
drives Beta Cell Dysfunction
0.0
0.00× baseline
 600.0
6.00× baseline
 0.0
0.00× baseline
 Hyperglycemia
GLP-1 receptor agonist (secretion + reduced hepatic output)
drives Incretin Axis Dysfunction
260.571429
0.87× baseline
 100.0
1.00× baseline
 12.16
1.22× baseline
 none
Insulin therapy (increased net insulin action)
drives Hyperglycemia
240.0
0.80× baseline
 100.0
1.00× baseline
 8.0
0.80× baseline
 none
Metformin + SGLT2 inhibitor (severe disease)
drives Hyperglycemia
244.8
0.82× baseline
 100.0
1.00× baseline
 8.16
0.82× baseline
 none
Derived artifact, regenerated by just gen-model-results — not curated evidence. Values rounded to 6 decimals. Config 41276c6a7082, BIOMD0000000341.xml e86507d1dc05. Phenotypes activate per the thresholds in the table above.
Pathograph links
Insulin Resistance Reduced whole-body insulin sensitivity is the model parameter si; lowering si (or PPARG/thiazolidinedione perturbation of si) reproduces the insulin-resistance driver of type 2 diabetes.
Beta Cell Dysfunction Maximal per-beta-cell secretory capacity (sigma) and the beta-cell-mass state variable (B) capture secretory failure and glucotoxic beta-cell-mass loss; TCF7L2/KCNJ11/HNF1A perturbations act here.
Hepatic Glucose Overproduction Insulin-independent hepatic glucose output is the parameter R0, the target reduced by metformin in the simulated treatment scenarios.
Findings
For normal parameters the model is bistable, with a physiological (euglycemic) and a pathological (hyperglycemic, beta-cell-collapse) steady state separated by a saddle - the dynamical basis for decompensation to overt diabetes.
Show evidence (1 reference)
PMID:11013117 SUPPORT Computational
"For normal parameter values, the model has two stable fixed points (representing physiological and pathological steady states), separated on a slow manifold by a saddle point"
Confirms the bistable, saddle-separated structure that lets an impairing perturbation tip the at-risk state to overt diabetes while a corrective treatment restores euglycemia.
The model defines three routes to prolonged hyperglycemia (regulated hyperglycemia, saddle-node bifurcation, and dynamical hyperglycemia), mapping onto insulin-resistance, secretory-failure, and hepatic-output drivers of type 2 diabetes.
Show evidence (1 reference)
PMID:11013117 SUPPORT Computational
"The model predicts that there are three pathways in prolonged hyperglycemia"
Grounds the perturbation scenarios: each disease driver corresponds to one of the model's predicted pathways into sustained hyperglycemia.
Wired for dismech-perturb (models/BIOMD0000000341.config.yaml). The disease-severity dial is insulin sensitivity si (baseline_gfr 0.72 = healthy); the deposited initial state (G=250 mg/dL) sits on the model's unstable saddle, i.e. the metabolically at-risk / impaired-fasting tipping point. Glucose-lowering treatments are simulated as parameter changes: metformin (R0 down), thiazolidinedione (si up), SGLT2 inhibitor (Eg0 up, insulin-independent), sulfonylurea/GLP-1 (sigma up), insulin therapy (net insulin action up). Insulin-independent therapies (SGLT2 inhibition, metformin) and sensitizers (TZD) recompensate the model to euglycemia, whereas a pure secretagogue fails once beta-cell mass has collapsed - reproducing secondary secretagogue failure in advanced disease. Thresholds are calibrated to model steady-state values, not clinical reference ranges.
Show evidence (1 reference)
PMID:11013117 SUPPORT Computational
"which consists of a system of three nonlinear ordinary differential equations, where glucose and insulin dynamics are fast relative to beta-cell mass dynamics"
Establishes the Topp model as a three-ODE dynamical model of beta-cell mass, insulin, and glucose - the substrate used here for simulating type 2 diabetes mechanisms and treatments.
Pancreatic Beta Cell Genome-Scale Metabolic Model GENOME_SCALE_METABOLIC
First comprehensive genome-scale metabolic reconstruction of human pancreatic beta cells, integrating transcriptomic data from healthy and type 2 diabetic islets. The model captures beta cell-specific metabolic pathways and identifies metabolic alterations in T2D including impaired glucose-stimulated insulin secretion mechanisms.
PMID:35276551
PLOS Computational Biology 2022 - context-specific reconstruction using RNA-seq from healthy and T2D beta cells
Whole-Body Human Metabolic Model for Diabetes GENOME_SCALE_METABOLIC
Multi-organ metabolic model (Harvey/Harvetta) capturing inter-organ metabolic fluxes in diabetes. Models liver, muscle, adipose, and pancreas metabolism with tissue-specific constraints derived from omics data.
Repository ↗ PMID:32472720 Base model: Recon3D
Predicts diabetes biomarkers and drug effects across multiple organs
PBPK Model for GLP-1 Receptor Agonists PHYSIOLOGICAL
Physiologically-based pharmacokinetic model for GLP-1 receptor agonists (semaglutide, tirzepatide) in T2D patients. Incorporates drug absorption, distribution, and receptor binding kinetics to optimize dosing regimens.
Used in clinical trial design and dose optimization for incretin-based therapies
AGORA2 Gut Microbiome Metabolic Models GENOME_SCALE_METABOLIC
Collection of 7,302 strain-resolved genome-scale metabolic reconstructions of human gut microorganisms. Enables personalized microbiome-host metabolic modeling by integrating with human metabolic models (Recon3D). Captures strain-level variation in SCFA production, bile acid metabolism, and drug biotransformation relevant to T2D.
Repository ↗ PMID:36543475
Nature Biotechnology 2022 - includes drug metabolism capabilities for 98 drugs; enables community-level FBA with MICOM
MICOM Community Metabolic Model COBRApy GENOME_SCALE_METABOLIC
Metagenome-scale modeling framework for simulating metabolic interactions in the gut microbiota. Integrates dietary constraints and taxon abundances from metagenomic data to predict personalized SCFA production, cross-feeding networks, and metabolic fluxes. Applied to T2D to study dysbiosis effects on butyrate production and glucose-insulin signaling.
PMID:31964767
mSystems 2020 - enables personalized microbiome metabolic modeling from 16S/metagenomics data
{ }

Source YAML

click to show 
name: Type 2 Diabetes Mellitus
creation_date: '2025-12-18T17:01:35Z'
description: >-
  Type 2 diabetes mellitus is a common metabolic disease defined by chronic
  hyperglycemia arising from the combination of peripheral insulin resistance
  and progressive pancreatic beta-cell dysfunction. Excess adiposity, physical
  inactivity, and polygenic susceptibility drive impaired insulin signaling in
  muscle, liver, and adipose tissue, with compensatory hyperinsulinemia that
  eventually fails as beta-cell secretory capacity declines. Hepatic glucose
  overproduction and incretin axis dysfunction further worsen glycemic control,
  and sustained hyperglycemia leads to microvascular and macrovascular
  complications such as retinopathy, nephropathy, and peripheral neuropathy.
category: Complex
parents:
- Diabetes Mellitus
- Metabolic Disease
- Endocrine Disease
disease_term:
  preferred_term: type 2 diabetes mellitus
  term:
    id: MONDO:0005148
    label: type 2 diabetes mellitus
gene_sets:
- gene_set: MYGENESET:KEGG_TYPE_II_DIABETES_MELLITUS
  relationship: CANONICAL_PATHWAY
  note: >-
    KEGG type II diabetes mellitus pathway.
pathophysiology:
- name: Impaired GLUT4-Mediated Glucose Uptake
  description: >
    Dysregulation of GLUT4 trafficking in adipocytes and skeletal muscle reduces
    insulin-stimulated glucose uptake. GULP1 facilitates GLUT4 translocation to
    the plasma membrane by counteracting ACAP1 inhibition of ARF6 activity.
    Reduced GULP1 activity therefore decreases peripheral glucose disposal and
    contributes to systemic insulin resistance.
  genes:
  - preferred_term: GULP1
    term:
      id: hgnc:18649
      label: GULP1
  - preferred_term: ACAP1
    term:
      id: hgnc:16467
      label: ACAP1
  - preferred_term: ARF6
    term:
      id: hgnc:659
      label: ARF6
  cell_types:
  - preferred_term: Adipocyte
    term:
      id: CL:0000136
      label: adipocyte
  - preferred_term: Skeletal Muscle Cell
    term:
      id: CL:0000188
      label: cell of skeletal muscle
  biological_processes:
  - preferred_term: Insulin Receptor Signaling
    term:
      id: GO:0008286
      label: insulin receptor signaling pathway
  - preferred_term: Protein Transport
    term:
      id: GO:0015031
      label: protein transport
  evidence:
  - reference: PMID:42436120
    reference_title: "GULP1 enhances GLUT4 translocation by counteracting ACAP1-ARF6 inhibition."
    supports: SUPPORT
    evidence_source: MODEL_ORGANISM
    snippet: "GULP1 significantly enhanced glucose uptake in adipocytes and muscle cells by promoting GLUT4 translocation to the plasma membrane. In obese mice, GULP1 overexpression improved insulin sensitivity and glucose tolerance."
    explanation: >-
      Demonstrates GULP1-dependent GLUT4 trafficking and improved insulin
      sensitivity in metabolic tissues and obese mice.
  - reference: PMID:42436120
    reference_title: "GULP1 enhances GLUT4 translocation by counteracting ACAP1-ARF6 inhibition."
    supports: SUPPORT
    evidence_source: MODEL_ORGANISM
    snippet: "Mechanistically, GULP1 counteracted ACAP1's inhibition of ARF6 activity, thereby facilitating insulin-stimulated GLUT4 trafficking."
    explanation: >-
      Establishes the GULP1-ACAP1-ARF6 regulatory mechanism.
  downstream:
  - target: Insulin Resistance
    description: >-
      Impaired GLUT4-mediated uptake reduces peripheral glucose disposal and
      contributes to systemic insulin resistance.
    causal_link_type: DIRECT
    evidence:
    - reference: PMID:42436120
      reference_title: "GULP1 enhances GLUT4 translocation by counteracting ACAP1-ARF6 inhibition."
      supports: SUPPORT
      evidence_source: MODEL_ORGANISM
      snippet: "GULP1 can bind to ACAP1 and ARF6, and alleviate the inhibitory effect of ACAP1 on ARF6 activity, thereby enhancing insulin-mediated glucose uptake and improving insulin sensitivity in obese mice."
      explanation: >-
        Directly links the trafficking axis to insulin-mediated glucose uptake
        and insulin sensitivity in the obesity model.
- name: Insulin Resistance
  description: >
    Peripheral tissues (muscle, liver, adipose) become resistant to insulin action,
    requiring higher insulin levels to maintain glucose homeostasis. This leads to
    compensatory hyperinsulinemia and eventually beta cell exhaustion.
  genes:
  - preferred_term: PPARG
    term:
      id: hgnc:9236
      label: PPARG
  cell_types:
  - preferred_term: Hepatocyte
    term:
      id: CL:0000182
      label: hepatocyte
  - preferred_term: Skeletal Muscle Cell
    term:
      id: CL:0000188
      label: cell of skeletal muscle
  - preferred_term: Adipocyte
    term:
      id: CL:0000136
      label: adipocyte
  biological_processes:
  - preferred_term: Insulin Signaling
    term:
      id: GO:0008286
      label: insulin receptor signaling pathway
  evidence:
  - reference: PMID:12231074
    reference_title: "Pathogenesis of skeletal muscle insulin resistance in type 2 diabetes mellitus."
    supports: SUPPORT
    snippet: "Insulin resistance is caused by the decreased ability of peripheral target tissues (especially muscle) to respond properly to normal circulating concentrations of insulin."
    explanation: This establishes that skeletal muscle is a key site of insulin resistance in type 2 diabetes, with impaired response to normal insulin levels.
  - reference: PMID:12231074
    reference_title: "Pathogenesis of skeletal muscle insulin resistance in type 2 diabetes mellitus."
    supports: SUPPORT
    snippet: "These alterations in glucose transport activity are likely the result of dysregulation of intramyocellular fatty acid metabolism, whereby fatty acids cause insulin resistance by activation of a serine kinase cascade, leading to decreased insulin-stimulated insulin receptor substrate (IRS)-1 tyrosine phosphorylation and decreased IRS-1-associated phosphatidylinositol 3-kinase activity, a required step in insulin-stimulated glucose transport into muscle."
    explanation: This describes the molecular mechanism of insulin resistance involving fatty acid-induced serine kinase activation that impairs insulin receptor signaling through IRS-1 and PI3K.
  - reference: PMID:29939616
    reference_title: "Insulin Resistance."
    supports: SUPPORT
    snippet: "Insulin resistance impairs glucose disposal, resulting in a compensatory increase in beta-cell insulin production and hyperinsulinemia."
    explanation: This confirms that insulin resistance leads to compensatory hyperinsulinemia as beta cells attempt to overcome impaired glucose disposal in peripheral tissues.
  - reference: PMID:17463248
    reference_title: "A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "confirm that variants near TCF7L2, SLC30A8, HHEX, FTO, PPARG, and KCNJ11 are associated with T2D risk"
    explanation: This genome-wide association study confirms PPARG (the insulin-sensitizing nuclear receptor and thiazolidinedione drug target) as a genetic susceptibility locus for type 2 diabetes, tying it to insulin-resistance biology.
  downstream:
  - target: Beta Cell Dysfunction
    description: Sustained insulin resistance increases compensatory beta-cell insulin demand, contributing to eventual beta-cell failure.
    causal_link_type: INDIRECT_KNOWN_INTERMEDIATES
    intermediate_mechanisms:
    - Compensatory hyperinsulinemia and beta-cell secretory stress
    evidence:
    - reference: PMID:29939616
      reference_title: "Insulin Resistance."
      supports: SUPPORT
      evidence_source: OTHER
      snippet: "This vicious cycle continues until pancreatic beta-cell activity can no longer adequately meet the insulin demand created by insulin resistance, resulting in hyperglycemia."
      explanation: Directly supports progression from insulin-resistance-driven insulin demand to beta-cell failure.
  - target: Hepatic Glucose Overproduction
    description: Hepatic insulin resistance reduces insulin-mediated suppression of liver glucose production.
    causal_link_type: DIRECT
    evidence:
    - reference: PMID:32872570
      reference_title: "Pathophysiology of Type 2 Diabetes Mellitus."
      supports: SUPPORT
      evidence_source: OTHER
      snippet: "IR contributes to increased glucose production in the liver and decreased glucose uptake both in the muscle, liver and adipose tissue."
      explanation: Supports insulin resistance as a cause of increased hepatic glucose production.
- name: Beta Cell Dysfunction
  description: >
    Progressive loss of pancreatic beta cell function and mass leads to inadequate
    insulin secretion relative to insulin demand. Beta cell failure is the key
    determinant of disease progression.
  genes:
  - preferred_term: KCNJ11
    term:
      id: hgnc:6257
      label: KCNJ11
  - preferred_term: SLC30A8
    term:
      id: hgnc:20303
      label: SLC30A8
  cell_types:
  - preferred_term: Pancreatic Beta Cell
    term:
      id: CL:0000169
      label: type B pancreatic cell
  biological_processes:
  - preferred_term: Insulin Secretion
    term:
      id: GO:0030073
      label: insulin secretion
  evidence:
  - reference: PMID:37035220
    reference_title: "Pancreatic β-cell dysfunction in type 2 diabetes: Implications of inflammation and oxidative stress."
    supports: SUPPORT
    snippet: "Insulin resistance and pancreatic β-cell dysfunction are major pathological mechanisms implicated in the development and progression of type 2 diabetes (T2D)."
    explanation: This establishes beta cell dysfunction as a core pathological mechanism in type 2 diabetes development alongside insulin resistance.
  - reference: PMID:37035220
    reference_title: "Pancreatic β-cell dysfunction in type 2 diabetes: Implications of inflammation and oxidative stress."
    supports: SUPPORT
    snippet: "Predominant markers of inflammation such as C-reactive protein, tumor necrosis factor alpha, and interleukin-1β are consistently associated with β-cell failure in preclinical models and in people with T2D."
    explanation: This demonstrates that inflammatory markers are associated with beta cell failure, indicating inflammation contributes to beta cell dysfunction.
  - reference: PMID:37035220
    reference_title: "Pancreatic β-cell dysfunction in type 2 diabetes: Implications of inflammation and oxidative stress."
    supports: SUPPORT
    snippet: "Similarly, important markers of oxidative stress, such as increased reactive oxygen species and depleted intracellular antioxidants, are consistent with pancreatic β-cell damage in conditions of T2D."
    explanation: This confirms that oxidative stress, characterized by increased ROS and depleted antioxidants, contributes to pancreatic beta cell damage in type 2 diabetes.
  - reference: PMID:29939616
    reference_title: "Insulin Resistance."
    supports: SUPPORT
    snippet: "This vicious cycle continues until pancreatic beta-cell activity can no longer adequately meet the insulin demand created by insulin resistance, resulting in hyperglycemia."
    explanation: This describes the progression from compensatory beta cell hyperfunction to beta cell exhaustion and failure, leading to hyperglycemia.
  - reference: PMID:17463248
    reference_title: "A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "confirm that variants near TCF7L2, SLC30A8, HHEX, FTO, PPARG, and KCNJ11 are associated with T2D risk"
    explanation: This genome-wide association study confirms two beta-cell genes as type 2 diabetes susceptibility loci — KCNJ11 (Kir6.2 subunit of the beta-cell KATP channel governing insulin secretion) and SLC30A8 (the beta-cell zinc transporter involved in insulin granule storage).
  downstream:
  - target: Hyperglycemia
    description: Insufficient beta-cell insulin secretion limits glucose control and drives hyperglycemia.
    causal_link_type: DIRECT
    evidence:
    - reference: PMID:32872570
      reference_title: "Pathophysiology of Type 2 Diabetes Mellitus."
      supports: SUPPORT
      evidence_source: OTHER
      snippet: "In the case of β-cell dysfunction, insulin secretion is reduced, limiting the body’s capacity to maintain physiological glucose levels."
      explanation: Supports beta-cell dysfunction as a direct cause of impaired glucose homeostasis.
- name: Hepatic Glucose Overproduction
  description: >
    Impaired suppression of hepatic gluconeogenesis leads to elevated fasting
    glucose levels. The liver fails to respond appropriately to insulin signals.
  cell_types:
  - preferred_term: Hepatocyte
    term:
      id: CL:0000182
      label: hepatocyte
  biological_processes:
  - preferred_term: Gluconeogenesis
    term:
      id: GO:0006094
      label: gluconeogenesis
  evidence:
  - reference: PMID:30150719
    reference_title: "Metformin reduces liver glucose production by inhibition of fructose-1-6-bisphosphatase."
    supports: SUPPORT
    snippet: "Diabetes is characterized by impaired glucose homeostasis partly due to abnormally elevated hepatic glucose production (HGP)."
    explanation: This establishes that elevated hepatic glucose production is a key feature of diabetes pathophysiology.
  - reference: PMID:30150719
    reference_title: "Metformin reduces liver glucose production by inhibition of fructose-1-6-bisphosphatase."
    supports: SUPPORT
    snippet: "Metformin exerts its antihyperglycemic action primarily through lowering hepatic glucose production (HGP)."
    explanation: This confirms that hepatic glucose overproduction is central to diabetes hyperglycemia, as metformin's primary mechanism targets HGP suppression.
  - reference: PMID:30150719
    reference_title: "Metformin reduces liver glucose production by inhibition of fructose-1-6-bisphosphatase."
    supports: SUPPORT
    snippet: "FBP1 catalyzes the irreversible hydrolysis of fructose-1,6-bisphosphate (F-1,6-P2) to fructose-6-phosphate (F6P) and inorganic phosphate (Pi) in the presence of divalent cations. FBP1 is a key rate-controlling enzyme in the gluconeogenic pathway."
    explanation: This identifies fructose-1,6-bisphosphatase (FBP1) as a key rate-controlling enzyme in hepatic gluconeogenesis, the pathway responsible for glucose overproduction in diabetes.
  downstream:
  - target: Hyperglycemia
    description: Excess hepatic glucose production contributes directly to impaired glucose homeostasis.
    causal_link_type: DIRECT
    evidence:
    - reference: PMID:30150719
      reference_title: "Metformin reduces liver glucose production by inhibition of fructose-1-6-bisphosphatase."
      supports: SUPPORT
      evidence_source: OTHER
      snippet: "Diabetes is characterized by impaired glucose homeostasis partly due to abnormally elevated hepatic glucose production (HGP)."
      explanation: Supports hepatic glucose overproduction as a contributor to diabetes-associated hyperglycemia.
- name: Mitochondrial Dysfunction and Oxidative Stress
  description: >
    Early-onset mitochondrial dysfunction and pathological reactive oxygen species
    (ROS) generation occur across multiple metabolic tissues including pancreatic
    beta cells, skeletal muscle, and adipose tissue. Impaired mitophagy and
    mitochondrial dynamics contribute to disease progression. Extracellular
    vesicle-mediated inter-organ miscommunication propagates oxidative damage.
  cell_types:
  - preferred_term: Pancreatic Beta Cell
    term:
      id: CL:0000169
      label: type B pancreatic cell
  - preferred_term: Skeletal Muscle Cell
    term:
      id: CL:0000188
      label: cell of skeletal muscle
  biological_processes:
  - preferred_term: Oxidative Stress Response
    term:
      id: GO:0006979
      label: response to oxidative stress
  - preferred_term: Mitophagy
    term:
      id: GO:0000422
      label: autophagy of mitochondrion
  evidence:
  - reference: PMID:38338783
    reference_title: "Mitochondrial Dysfunction, Oxidative Stress, and Inter-Organ Miscommunications in T2D Progression."
    supports: SUPPORT
    snippet: "New evidence suggests that T2D-lean individuals experience early β-cell dysfunction without significant IR. Regardless of the primary event (i.e., IR vs. β-cell dysfunction) that contributes to dysglycemia, significant early-onset oxidative damage and mitochondrial dysfunction in multiple metabolic tissues may be a driver of T2D onset and progression."
    explanation: This establishes that mitochondrial dysfunction and oxidative damage occur early and may drive T2D progression regardless of whether insulin resistance or beta cell dysfunction is the primary event.
  - reference: PMID:38338783
    reference_title: "Mitochondrial Dysfunction, Oxidative Stress, and Inter-Organ Miscommunications in T2D Progression."
    supports: SUPPORT
    snippet: "Physiological oxidative stress promotes inter-tissue communication, while pathological oxidative stress promotes inter-tissue mis-communication, and new evidence suggests that this is mediated via extracellular vesicles (EVs), including mitochondria containing EVs."
    explanation: This describes the novel mechanism of extracellular vesicle-mediated oxidative stress propagation between tissues in T2D pathogenesis.
  - reference: PMID:37035220
    reference_title: "Pancreatic β-cell dysfunction in type 2 diabetes: Implications of inflammation and oxidative stress."
    supports: SUPPORT
    snippet: "Similarly, important markers of oxidative stress, such as increased reactive oxygen species and depleted intracellular antioxidants, are consistent with pancreatic β-cell damage in conditions of T2D."
    explanation: This confirms that oxidative stress characterized by increased ROS and depleted antioxidants contributes to beta cell damage.
- name: Incretin Axis Dysfunction
  description: >
    Impaired incretin hormone signaling, particularly blunted glucose-dependent
    insulinotropic peptide (GIP) action in beta cells. GLP-1 action is relatively
    preserved. The incretin effect amplifies insulin secretion in response to
    oral glucose via cAMP-PKA signaling pathways.
  genes:
  - preferred_term: TCF7L2
    term:
      id: hgnc:11641
      label: TCF7L2
  cell_types:
  - preferred_term: Pancreatic Beta Cell
    term:
      id: CL:0000169
      label: type B pancreatic cell
  - preferred_term: Enteroendocrine Cell
    term:
      id: CL:0000164
      label: enteroendocrine cell
  biological_processes:
  - preferred_term: cAMP Signaling
    term:
      id: GO:0141156
      label: cAMP/PKA signal transduction
  - preferred_term: Insulin Secretion Regulation
    term:
      id: GO:0050796
      label: regulation of insulin secretion
  evidence:
  - reference: PMID:38831203
    reference_title: "GLP1-GIP receptor co-agonists: a promising evolution in the treatment of type 2 diabetes."
    supports: SUPPORT
    snippet: "Dual glucagon like peptide 1 (GLP1) and glucose-dependent insulinotropic peptide (GIP) receptor agonists are among the new pharmacological strategies recently developed to address this challenge."
    explanation: This establishes the importance of the GLP-1/GIP incretin axis in T2D pathophysiology and its targeting by dual agonist therapies.
  - reference: PMID:38831203
    reference_title: "GLP1-GIP receptor co-agonists: a promising evolution in the treatment of type 2 diabetes."
    supports: SUPPORT
    snippet: "Tirzepatide, characterized by its ability to selectively bind and activate receptors for the intestinal hormones GIP and GLP-1, has been tested in numerous clinical studies and is already currently authorized in several countries for the treatment of type 2 diabetes and obesity."
    explanation: This demonstrates the clinical relevance of incretin axis dysfunction by showing dual GLP-1/GIP agonism is effective for T2D treatment.
  - reference: PMID:19934000
    reference_title: "TCF7L2 variant rs7903146 affects the risk of type 2 diabetes by modulating incretin action."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "The TCF7L2 variant rs7903146 appears to affect risk of type 2 diabetes, at least in part, by modifying the effect of incretins on insulin secretion."
    explanation: Supports TCF7L2 as a genetic contributor to incretin-axis effects on insulin secretion in humans.
  downstream:
  - target: Beta Cell Dysfunction
    description: Reduced beta-cell sensitivity to incretins impairs oral-glucose-stimulated insulin secretion.
    causal_link_type: DIRECT
    evidence:
    - reference: PMID:19934000
      reference_title: "TCF7L2 variant rs7903146 affects the risk of type 2 diabetes by modulating incretin action."
      supports: SUPPORT
      evidence_source: HUMAN_CLINICAL
      snippet: "This is not due to reduced secretion of GLP-1 and GIP but rather due to the effect of TCF7L2 on the sensitivity of the beta-cell to incretins."
      explanation: Directly supports a TCF7L2-associated incretin-sensitivity defect at the beta cell.
- name: Chronic Hyperglycemia
  description: >-
    Sustained hyperglycemia arising from the combined insulin resistance,
    beta-cell secretory failure, and hepatic glucose overproduction is the shared
    driver of long-term diabetic tissue injury and the entry point to the
    conserved diabetic vascular-complication cascade captured by the
    diabetic_vascular_complications module (chronic hyperglycemia -> oxidative
    and AGE-RAGE stress -> endothelial dysfunction and vascular inflammation ->
    micro- and macrovascular injury -> end-organ complications). In type 2
    diabetes this cascade manifests clinically as diabetic retinopathy,
    neuropathy, nephropathy, and atherosclerotic cardiovascular disease.
  conforms_to: "diabetic_vascular_complications#Chronic Hyperglycemia"
  biological_processes:
  - preferred_term: glucose homeostasis
    term:
      id: GO:0042593
      label: glucose homeostasis
    modifier: ABNORMAL
  downstream:
  - target: Diabetic Retinopathy
    description: >-
      Sustained hyperglycemia drives the microvascular injury that produces
      diabetic retinopathy, one of the shared end-organ complications captured by
      the diabetic_vascular_complications module this node conforms to.
    evidence:
    - reference: PMID:39158206
      reference_title: "Serum biomarkers for predicting microvascular complications of diabetes mellitus."
      supports: SUPPORT
      evidence_source: HUMAN_CLINICAL
      snippet: "Diabetic microvascular complications such as retinopathy, nephropathy, and neuropathy are primary causes of blindness, terminal renal failure, and neuropathic disorders in type 2 diabetes mellitus patients."
      explanation: >-
        Supports chronic hyperglycemia driving retinopathy as a microvascular
        end-organ complication in type 2 diabetes.
  - target: Peripheral Neuropathy
    description: >-
      Sustained hyperglycemia drives the microvascular and metabolic nerve injury
      that produces diabetic peripheral neuropathy, another shared end-organ
      complication of the conserved vascular-complication cascade.
    evidence:
    - reference: PMID:39158206
      reference_title: "Serum biomarkers for predicting microvascular complications of diabetes mellitus."
      supports: SUPPORT
      evidence_source: HUMAN_CLINICAL
      snippet: "Diabetic microvascular complications such as retinopathy, nephropathy, and neuropathy are primary causes of blindness, terminal renal failure, and neuropathic disorders in type 2 diabetes mellitus patients."
      explanation: >-
        Supports chronic hyperglycemia driving neuropathy as a microvascular
        end-organ complication in type 2 diabetes.
  evidence:
  - reference: PMID:29939616
    reference_title: "Insulin Resistance."
    supports: SUPPORT
    evidence_source: OTHER
    snippet: "This vicious cycle continues until pancreatic beta-cell activity can no longer adequately meet the insulin demand created by insulin resistance, resulting in hyperglycemia."
    explanation: >-
      Supports sustained hyperglycemia arising from beta-cell failure to
      compensate for insulin resistance, the shared trigger that feeds the
      diabetic vascular-complication cascade.
mechanistic_hypotheses:
- hypothesis_group_id: pgs_context_amplification
  hypothesis_label: Amplification of polygenic T2D risk in adverse metabolic contexts via shared insulin-resistance convergence
  status: EMERGING
  description: >-
    Polygenic-score-by-context (PGS×C) interactions reported for type 2 diabetes
    in the UK Biobank appear to reflect amplification rather than
    context-specific causal variants: the same susceptibility loci (e.g. TCF7L2,
    PPARG, KCNJ11, SLC30A8) exert systematically larger effects in
    disease-promoting metabolic contexts. This entry proposes that the
    amplification arises because polygenic liability and adverse exposures
    converge on the shared Insulin Resistance node (with Beta Cell Dysfunction as
    a parallel target), so their joint effect on the liability-threshold scale is
    super-additive rather than additive. Nagpal & Gibson (Nat Genet 2026,
    PMID:42443528) highlight the interaction between reduced
    polyunsaturated fatty acids (low omega-6) and high glucose, which elevates
    T2D risk increasingly as the PGS rises, and identify sex and sex-adjusted
    testosterone as further amplifying contexts.
  evidence:
  - reference: PMID:42443528
    reference_title: "Pervasive interactions between exposures and polygenic risk can inform more effective clinical and behavioral interventions."
    supports: SUPPORT
    evidence_source: COMPUTATIONAL
    snippet: The predominant mechanism for PGS×C is the amplification of genetic effects in adverse contexts, such as low polyunsaturated fatty acids or social determinants of ill health
    explanation: >-
      Direct source (Nagpal & Gibson 2026): across seven UK Biobank diseases and
      75 contexts, amplification of genetic effects in adverse contexts is
      identified as the predominant mechanism of PGS×context interaction — the
      mechanism applied in this hypothesis.
  - reference: PMID:37228747
    reference_title: "Amplification is the primary mode of gene-by-sex interaction in complex human traits."
    supports: SUPPORT
    evidence_source: COMPUTATIONAL
    snippet: GxSex is pervasive but acts primarily through systematic sex differences in the magnitude of many genetic effects
    explanation: >-
      Establishes amplification — systematic differences in the magnitude of
      polygenic effects rather than in the identity of causal variants — as the
      primary mode of gene-by-sex interaction across physiological traits, and
      notes that testosterone may mediate this amplification. Cited as general
      support for amplification as a mode of PGS×context interaction; the paper
      is not T2D-specific.
  notes: >-
    EMERGING hypothesis motivated by population-scale PGS×context analyses
    (primary source PMID:42443528; general amplification mechanism corroborated
    by PMID:37228747, which documents testosterone-mediated amplification). The
    convergence claim (polygenic liability + adverse metabolic context → Insulin
    Resistance) is a mechanistic interpretation and is not itself established as
    causal — see the reverse-causation knowledge gap under discussions.
phenotypes:
- name: Hyperglycemia
  category: Metabolic
  frequency: VERY_FREQUENT
  diagnostic: true
  phenotype_term:
    preferred_term: Hyperglycemia
    term:
      id: HP:0003074
      label: Hyperglycemia
  evidence:
  - reference: PMID:29939616
    reference_title: "Insulin Resistance."
    supports: SUPPORT
    snippet: "This vicious cycle continues until pancreatic beta-cell activity can no longer adequately meet the insulin demand created by insulin resistance, resulting in hyperglycemia."
    explanation: This describes how the failure of beta cells to compensate for insulin resistance results in hyperglycemia, the hallmark of type 2 diabetes.
  - reference: PMID:30150719
    reference_title: "Metformin reduces liver glucose production by inhibition of fructose-1-6-bisphosphatase."
    supports: SUPPORT
    snippet: "Diabetes is characterized by impaired glucose homeostasis partly due to abnormally elevated hepatic glucose production (HGP)."
    explanation: This confirms that hyperglycemia in diabetes results from elevated hepatic glucose production and impaired glucose homeostasis.
- name: Polydipsia
  category: Systemic
  frequency: FREQUENT
  phenotype_term:
    preferred_term: Polydipsia
    term:
      id: HP:0001959
      label: Polydipsia
  evidence:
  - reference: PMID:9398128
    reference_title: "Factors contributing to the degree of polyuria in a patient with poorly controlled diabetes mellitus."
    supports: SUPPORT
    snippet: "Polyuria due to a glucose-induced osmotic diuresis is common in patients with hyperglycemia."
    explanation: This establishes that polyuria results from glucose-induced osmotic diuresis in hyperglycemia, which in turn leads to polydipsia as a compensatory response to fluid loss.
- name: Polyuria
  category: Renal
  frequency: FREQUENT
  phenotype_term:
    preferred_term: Polyuria
    term:
      id: HP:0000103
      label: Polyuria
  evidence:
  - reference: PMID:9398128
    reference_title: "Factors contributing to the degree of polyuria in a patient with poorly controlled diabetes mellitus."
    supports: SUPPORT
    snippet: "Polyuria due to a glucose-induced osmotic diuresis is common in patients with hyperglycemia. This diuresis usually abates when the plasma glucose level approaches its renal threshold."
    explanation: This describes the mechanism of polyuria in diabetes as glucose-induced osmotic diuresis when plasma glucose exceeds the renal threshold.
- name: Obesity
  category: Metabolic
  frequency: FREQUENT
  phenotype_term:
    preferred_term: Obesity
    term:
      id: HP:0001513
      label: Obesity
- name: Fatigue
  category: Systemic
  frequency: FREQUENT
  phenotype_term:
    preferred_term: Fatigue
    term:
      id: HP:0012378
      label: Fatigue
- name: Insulin Resistance
  category: Metabolic
  frequency: VERY_FREQUENT
  diagnostic: true
  phenotype_term:
    preferred_term: Insulin Resistance
    term:
      id: HP:0000855
      label: Insulin resistance
  evidence:
  - reference: PMID:12231074
    reference_title: "Pathogenesis of skeletal muscle insulin resistance in type 2 diabetes mellitus."
    supports: SUPPORT
    snippet: "Insulin resistance is caused by the decreased ability of peripheral target tissues (especially muscle) to respond properly to normal circulating concentrations of insulin."
    explanation: This establishes insulin resistance as a hallmark feature of T2D pathophysiology.
- name: Impaired Glucose Tolerance
  category: Metabolic
  frequency: VERY_FREQUENT
  phenotype_term:
    preferred_term: Impaired Glucose Tolerance
    term:
      id: HP:0001952
      label: Glucose intolerance
- name: Hyperinsulinemia
  category: Metabolic
  frequency: FREQUENT
  notes: Compensatory response to insulin resistance
  phenotype_term:
    preferred_term: Hyperinsulinemia
    term:
      id: HP:0000842
      label: Hyperinsulinemia
  evidence:
  - reference: PMID:29939616
    reference_title: "Insulin Resistance."
    supports: SUPPORT
    snippet: "Insulin resistance impairs glucose disposal, resulting in a compensatory increase in beta-cell insulin production and hyperinsulinemia."
    explanation: This establishes hyperinsulinemia as a compensatory response to insulin resistance in T2D.
- name: Diabetic Retinopathy
  category: Ophthalmologic
  frequency: OCCASIONAL
  notes: Long-term microvascular complication
  phenotype_term:
    preferred_term: Retinopathy
    term:
      id: HP:0000488
      label: Retinopathy
- name: Peripheral Neuropathy
  category: Neurological
  frequency: OCCASIONAL
  notes: Long-term microvascular complication
  phenotype_term:
    preferred_term: Peripheral Neuropathy
    term:
      id: HP:0009830
      label: Peripheral neuropathy
biochemical:
- name: Myeloid Lineage Plasma Proteomic Age Gap
  biomarker_term:
    preferred_term: myeloid lineage plasma proteomic aging clock (age gap)
    term:
      id: NCIT:C97139
      label: Proteomic Profile
  presence: Elevated
  context: >-
    Blood-based cell-type-specific aging clock. Plasma proteins are mapped to
    their putative cell of origin using Human Protein Atlas single-cell
    transcriptomic data, and a machine-learning model estimates myeloid lineage
    biological age; the age gap is the difference between that estimate and
    chronological age. "Extreme" agers are those in the upper tail of the age-gap
    distribution.
  cell_types:
  - preferred_term: Myeloid lineage cell
    term:
      id: CL:0000763
      label: myeloid cell
  notes: >-
    Myeloid lineage aging was the strongest cellular-aging predictor of incident
    type 2 diabetes in UK Biobank (HR 3.88, 95% CI 3.33-4.52) and remained
    significant after adjustment for established risk factors including HbA1c,
    BMI, smoking and renal function. Its distinctive value is that it stratified
    risk among people who were still normoglycemic, so it identifies future cases
    before the glycemic measures used to diagnose them move. Type 1 diabetes and
    other specified diabetes were excluded from the incidence analysis. This is
    an observational prognostic association; the islet-inflammation reading below
    is the authors' mechanistic interpretation, not something the clock measures.
  evidence:
  - reference: PMID:42297981
    reference_title: "Plasma proteomic signatures of cellular aging predict human disease."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: myeloid lineage extreme aging demonstrated the strongest prognostic value
    explanation: >-
      Establishes myeloid lineage aging as the leading cellular-aging predictor
      of incident type 2 diabetes among the cell types tested.
  - reference: PMID:42297981
    reference_title: "Plasma proteomic signatures of cellular aging predict human disease."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: myeloid lineage aging identified normoglycemic individuals at higher type 2 diabetes risk
    explanation: >-
      States the clinically distinctive claim: risk stratification before
      glycemic criteria are met, which is what would make the marker useful for
      targeted surveillance.
  readouts:
  - target: Beta Cell Dysfunction
    relationship: PREDICTS
    direction: POSITIVE
    endpoint_context: PROGNOSTIC
    interpretation: >-
      An elevated myeloid lineage age gap predicts incident type 2 diabetes in
      still-normoglycemic individuals, and is read against beta cell dysfunction
      via the proposed route of myeloid-derived cytokines inflaming the islet
      microenvironment. The prognostic association is measured; the islet route
      is inference and should not be curated as an established causal edge.
    evidence:
    - reference: PMID:42297981
      reference_title: "Plasma proteomic signatures of cellular aging predict human disease."
      supports: SUPPORT
      evidence_source: HUMAN_CLINICAL
      snippet: consistent with a role of myeloid-derived cytokines in the initiation of an inflamed pancreatic islet microenvironment and increased susceptibility to type 2 diabetes
      explanation: >-
        The authors' stated mechanistic rationale linking the myeloid aging
        signature to the islet compartment, offered as consistency rather than
        as demonstrated causation.
- name: NMR metabolomic risk score (MetRS)
  specificity: >
    A research composite, not a clinical assay. MetRS is a machine-learning
    score built from the top 30 of 313 Nightingale Health NMR plasma measures,
    derived and replicated in UK Biobank. It is not validated for clinical use
    and does not replace fasting glucose or HbA1c.
  context: >
    In the UK Biobank metabolome-phenome atlas, MetRS classified prevalent type
    2 diabetes with an area under the curve of 0.941 and predicted incident
    diabetic complications, including diabetic maculopathy (0.921), diabetic
    kidney failure (0.919) and type 2 diabetes with peripheral circulatory
    complications (0.913). No presence value is recorded because MetRS is a
    derived multi-analyte score rather than a measured analyte. Two limits
    matter for interpretation: the score is trained and tested in a single,
    predominantly European-ancestry volunteer cohort, and a high classification
    area under the curve for prevalent disease partly reflects the metabolic
    consequences of established diabetes rather than antecedent risk.
  readouts:
  - target: Chronic Hyperglycemia
    relationship: CORRELATES_WITH
    direction: POSITIVE
    endpoint_context: DIAGNOSTIC
    interpretation: >
      The lipid, amino-acid and glycolysis-related measures that dominate MetRS
      shift with sustained hyperglycemia and insulin resistance, so the score
      reads out the systemic metabolic state rather than any single mechanism.
  evidence:
  - reference: PMID:40973818
    reference_title: "Mapping the plasma metabolome to human health and disease in 274,241 adults."
    supports: SUPPORT
    evidence_source: COMPUTATIONAL
    snippet: "MetRS offered favourable diagnostic and predictive performance, particularly in T2D"
    explanation: >
      Names type 2 diabetes as one of the conditions in which the
      machine-learning metabolomic risk score performed best, both for
      classification of prevalent disease and for prediction of incident
      disease.
  - reference: PMID:40973818
    reference_title: "Mapping the plasma metabolome to human health and disease in 274,241 adults."
    supports: SUPPORT
    evidence_source: COMPUTATIONAL
    snippet: "The MetRS witnessed excellent diagnosis for type 1 diabetes (T1D; AUC = 0.944), T2D (AUC = 0.941), diabetic maculopathy (AUC = 0.940) and chronic kidney disease (CKD; AUC = 0.933)."
    explanation: >
      Quotes the prevalent-disease classification figure of 0.941 for type 2
      diabetes that the context field reports.
  - reference: PMID:40973818
    reference_title: "Mapping the plasma metabolome to human health and disease in 274,241 adults."
    supports: SUPPORT
    evidence_source: COMPUTATIONAL
    snippet: "the MetRS excellently predicted future diabetic complications, for example, diabetic maculopathy (AUC = 0.921 (95% CI, 0.914-0.928)), diabetic kidney failure (AUC = 0.919 (0.906-0.930)) and type 2 diabetes (T2D) with peripheral circulatory complications (AUC = 0.913 (0.898-0.926))."
    explanation: >
      Quotes all three incident-complication figures reported in the context
      field, so the predictive claim and its numbers both rest on source text.
genetic:
- name: TCF7L2
  gene_term:
    preferred_term: TCF7L2
    term:
      id: hgnc:11641
      label: TCF7L2
  association: Risk Factor
- name: PPARG
  gene_term:
    preferred_term: PPARG
    term:
      id: hgnc:9236
      label: PPARG
  association: Risk Factor
- name: KCNJ11
  gene_term:
    preferred_term: KCNJ11
    term:
      id: hgnc:6257
      label: KCNJ11
  association: Risk Factor
- name: SLC30A8
  gene_term:
    preferred_term: SLC30A8
    term:
      id: hgnc:20303
      label: SLC30A8
  association: Risk Factor
environmental:
- name: Sedentary Lifestyle
  exposure_term:
    preferred_term: sedentary lifestyle
    term:
      id: ECTO:6000004
      label: exposure to sedentary lifestyle
  notes: Major modifiable risk factor
  evidence:
  - reference: PMID:22890825
    reference_title: "Sedentary time in adults and the association with diabetes, cardiovascular disease and death: systematic review and meta-analysis."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "The greatest sedentary time compared with the lowest was associated with a 112% increase in the RR of diabetes"
    explanation: Systematic review and meta-analysis quantifies sedentary time's association with type 2 diabetes risk.
- name: High-Calorie Diet
  exposure_term:
    preferred_term: high-calorie dietary pattern
    modifier: INCREASED
    term:
      id: XCO:0000013
      label: diet
  notes: Contributes to obesity and insulin resistance
  evidence:
  - reference: PMID:28065634
    reference_title: "Association between Dietary Energy Density and Incident Type 2 Diabetes in the Women's Health Initiative."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Risk of diabetes developing was 24% greater for women in the highest dietary energy density quintile compared with the lowest after adjusting for confounders (95% CI 1.17 to 1.32)"
    explanation: Prospective cohort study (Women's Health Initiative) links higher dietary energy density to increased type 2 diabetes incidence.
- name: Obesity
  notes: Primary risk factor for insulin resistance
  evidence:
  - reference: PMID:20493574
    reference_title: "The magnitude of association between overweight and obesity and the risk of diabetes: a meta-analysis of prospective cohort studies."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "The overall RR of diabetes for obese persons compared to those with normal weight was 7.19, 95% CI: 5.74, 9.00 and for overweight was 2.99, 95% CI: 2.42, 3.72"
    explanation: Meta-analysis of prospective cohort studies quantifies obesity's association with type 2 diabetes risk.
treatments:
- name: Metformin
  description: First-line oral medication that reduces hepatic glucose production and improves insulin sensitivity.
  treatment_term:
    preferred_term: targeted therapy
    term:
      id: NCIT:C93352
      label: Targeted Therapy
    therapeutic_agent:
    - preferred_term: metformin
      term:
        id: CHEBI:6801
        label: metformin
  target_mechanisms:
  - target: Hepatic Glucose Overproduction
    treatment_effect: INHIBITS
    description: >-
      Metformin's primary action is suppression of excessive hepatic
      gluconeogenesis, so it targets the hepatic-glucose-overproduction node
      directly (with a secondary insulin-sensitizing effect), lowering fasting
      glucose upstream of chronic hyperglycemia.
- name: Lifestyle Modification
  description: Diet and exercise interventions to reduce weight and improve metabolic health.
  treatment_term:
    preferred_term: Lifestyle Therapy
    term:
      id: NCIT:C15900
      label: Lifestyle Therapy
  target_mechanisms:
  - target: Insulin Resistance
    treatment_effect: INHIBITS
    description: >-
      Weight loss and exercise improve peripheral insulin sensitivity, acting on
      the insulin-resistance node that is the primary upstream driver of type 2
      diabetes rather than merely lowering glucose downstream.
- name: GLP-1 Receptor Agonists
  description: Injectable medications that enhance insulin secretion and promote weight loss.
  treatment_term:
    preferred_term: targeted therapy
    term:
      id: NCIT:C93352
      label: Targeted Therapy
  target_mechanisms:
  - target: Incretin Axis Dysfunction
    treatment_effect: INHIBITS
    description: >-
      GLP-1 receptor agonists restore the deficient incretin signal, correcting
      the incretin-axis-dysfunction node to enhance glucose-dependent insulin
      secretion (with weight loss and appetite effects), upstream of chronic
      hyperglycemia.
  evidence:
  - reference: PMID:38831203
    reference_title: "GLP1-GIP receptor co-agonists: a promising evolution in the treatment of type 2 diabetes."
    supports: SUPPORT
    snippet: "Dual glucagon like peptide 1 (GLP1) and glucose-dependent insulinotropic peptide (GIP) receptor agonists are among the new pharmacological strategies recently developed to address this challenge."
    explanation: This establishes GLP-1/GIP receptor agonists as effective pharmacological strategies for T2D treatment.
- name: SGLT2 Inhibitors
  description: Oral medications that increase urinary glucose excretion.
  treatment_term:
    preferred_term: Pharmacotherapy
    term:
      id: NCIT:C15986
      label: Pharmacotherapy
  target_mechanisms:
  - target: Chronic Hyperglycemia
    treatment_effect: INHIBITS
    description: >-
      SGLT2 inhibitors act insulin-independently, increasing urinary glucose
      excretion to lower chronic hyperglycemia directly (with additional
      cardiorenal protection), targeting the shared hyperglycemia node that feeds
      the diabetic vascular-complication cascade.
- name: Insulin Therapy
  description: Required when beta cell function declines significantly.
  treatment_term:
    preferred_term: insulin therapy
    term:
      id: NCIT:C179441
      label: Injected Insulin Diabetes Therapy
  target_mechanisms:
  - target: Chronic Hyperglycemia
    treatment_effect: INHIBITS
    description: >-
      When beta-cell secretory capacity has declined enough that endogenous
      insulin is inadequate, exogenous insulin replaces the shortfall and lowers
      chronic hyperglycemia directly — a later-line glucose-node intervention
      once upstream insulin-sensitizing and incretin approaches no longer suffice.
- name: GLP-1/GIP Dual Agonists
  description: Tirzepatide and similar agents that activate both GLP-1 and GIP receptors for enhanced glycemic control and weight loss.
  treatment_term:
    preferred_term: targeted therapy
    term:
      id: NCIT:C93352
      label: Targeted Therapy
  target_mechanisms:
  - target: Incretin Axis Dysfunction
    treatment_effect: INHIBITS
    description: >-
      Tirzepatide co-activates the GIP and GLP-1 receptors, correcting the
      incretin-axis-dysfunction node more completely than single GLP-1 agonism
      for enhanced glucose-dependent insulin secretion and weight loss.
  evidence:
  - reference: PMID:38831203
    reference_title: "GLP1-GIP receptor co-agonists: a promising evolution in the treatment of type 2 diabetes."
    supports: SUPPORT
    snippet: "Tirzepatide, characterized by its ability to selectively bind and activate receptors for the intestinal hormones GIP and GLP-1, has been tested in numerous clinical studies and is already currently authorized in several countries for the treatment of type 2 diabetes and obesity."
    explanation: This confirms tirzepatide as an authorized dual GLP-1/GIP agonist for T2D and obesity treatment.
discussions:
- discussion_id: t2d_pgsxc_reverse_causation
  prompt: >-
    Are the T2D PGS×context interactions driven by adverse exposures causally
    amplifying genetic risk, or are some "contexts" (notably circulating glucose)
    actually downstream readouts of incipient disease (reverse causation)?
  kind: KNOWLEDGE_GAP
  status: OPEN
  attaches_to:
  - pathophysiology#Insulin Resistance
  - environmental#Sedentary Lifestyle
  - environmental#High-Calorie Diet
  rationale: >-
    Population PGS×context analyses (Nagpal & Gibson 2026,
    PMID:42443528) are largely unable to establish the
    causality of specific contexts. For T2D this is especially acute for
    circulating glucose, a major amplifying "context" in the paper that is also
    nearly definitional of the disease and therefore substantially downstream of
    incipient hyperglycemia rather than a purely upstream driver. Behavioural
    contexts such as reduced physical activity and higher-calorie diet may also
    be partly responses to early metabolic decline. Distinguishing genuine
    amplification from reverse causation determines whether the modelled
    lifestyle interventions would actually reduce risk.
  proposed_experiments:
  - experiment_id: t2d_pgsxc_mr_direction
    name: Mendelian randomization of exposure-to-T2D direction across PGS strata
    description: >-
      Use bidirectional / multivariable Mendelian randomization to test whether
      each candidate context (glucose, omega-6 fatty acids, sex-adjusted
      testosterone, physical activity) causally affects T2D versus being a
      consequence of subclinical disease, and whether the causal effect estimate
      scales with polygenic liability as the amplification model predicts.
    decision_criterion: >-
      A context is retained as a causal amplifier if MR supports
      exposure-to-disease directionality and the exposure-attributable risk
      difference increases across increasing PGS strata; it is flagged as a
      reverse-causation suspect otherwise.
  - experiment_id: t2d_pgsxc_prospective_temporal
    name: Prospective incident-T2D analysis restricted to pre-diagnosis exposure windows
    description: >-
      Restrict exposures to measurements taken well before diagnosis and repeat
      the PGS×context liability-threshold modelling on incident cases only, to
      reduce the chance that exposure values (particularly glucose) reflect early
      disease rather than antecedent risk.
    decision_criterion: >-
      Amplification is supported if the PGS×context deviation from additivity
      persists when only pre-diagnosis exposure windows and incident cases are
      used.
- discussion_id: gap_t2d_beta_cell_dedifferentiation_reversibility
  prompt: >-
    Is beta-cell dedifferentiation and beta-to-alpha-like trajectory switching
    in type 2 diabetes a reversible driver of beta-cell failure, or mostly a
    late marker of intrapancreatic adiposity, inflammation, and metabolic stress?
  kind: KNOWLEDGE_GAP
  status: OPEN
  attaches_to:
  - pathophysiology#Beta Cell Dysfunction
  - pathophysiology#Insulin Resistance
  - pathophysiology#Incretin Axis Dysfunction
  rationale: >-
    The entry links insulin resistance to beta-cell dysfunction, but recent
    human-islet work points to a specific beta-cell identity-loss axis involving
    intrapancreatic adipocytes, immune recruitment, alpha-like trajectories, and
    SMOC1. Resolving whether this state is causally reversible would determine
    whether beta-cell recovery should be modeled as a targetable mechanism
    rather than only as clinical glycemic improvement.
  proposed_experiments:
  - experiment_id: exp_t2d_islet_adipocyte_beta_dedifferentiation_rescue
    name: Human islet-adipocyte beta-cell dedifferentiation rescue assay
    description: >-
      Co-culture human pancreatic islets with intrapancreatic adipocytes and
      autologous immune cells in a microphysiological system; impose
      glucolipotoxic stress; perturb SMOC1 and incretin signaling; and test
      whether beta-cell identity, insulin secretion, and beta-to-alpha-like
      trajectory markers recover when the adipocyte-inflammatory niche is
      removed or therapeutically modulated.
    experiment_type:
      preferred_term: human islet microphysiological perturbation experiment
    model_systems:
    - name: Human islet-adipocyte-immune microphysiological system
      description: >-
        Human islet organ-on-chip or perifusion coculture pairing islets with
        adipocytes and immune cells to model the pancreatic fat-associated
        microenvironment implicated in beta-cell identity loss.
      experimental_model_type: ORGAN_ON_CHIP
      namo_type: namo:OrganOnChip
      organism:
        preferred_term: human
        term:
          id: NCBITaxon:9606
          label: Homo sapiens
      tissue_term:
        preferred_term: pancreas
        term:
          id: UBERON:0001264
          label: pancreas
      cell_types:
      - preferred_term: pancreatic beta cell
        term:
          id: CL:0000169
          label: type B pancreatic cell
      - preferred_term: pancreatic alpha cell
        term:
          id: CL:0000171
          label: pancreatic A cell
      - preferred_term: adipocyte
        term:
          id: CL:0000136
          label: adipocyte
      - preferred_term: T cell
        term:
          id: CL:0000084
          label: T cell
      cell_source: donor human islets with matched or donor-compatible pancreatic adipocytes and immune cells
      culture_system: perfused islet-on-chip or dynamic perifusion coculture
    perturbations:
    - name: Pancreatic adipocyte inflammatory niche
      target: pathophysiology#Beta Cell Dysfunction
      description: >-
        Adipocyte proximity plus inflammatory immune-cell recruitment used to
        model intrapancreatic fat-associated beta-cell stress.
      biological_processes:
      - preferred_term: inflammatory response
        term:
          id: GO:0006954
          label: inflammatory response
    - name: SMOC1 gain and loss of function
      target: pathophysiology#Beta Cell Dysfunction
      description: >-
        Beta-cell SMOC1 overexpression and knockdown used to test whether the
        alpha-cell-associated trajectory gene is sufficient and necessary for
        beta-cell dedifferentiation.
      gene:
        preferred_term: SMOC1
        term:
          id: hgnc:20318
          label: SMOC1
    - name: GLP-1/GIP receptor agonist rescue
      target: pathophysiology#Incretin Axis Dysfunction
      description: >-
        Incretin receptor agonist exposure used to test whether clinically
        relevant treatment restores beta-cell function and identity under an
        adipocyte-inflammatory niche.
      treatment_term:
        preferred_term: targeted therapy
        term:
          id: NCIT:C93352
          label: Targeted Therapy
    readouts:
    - name: Glucose-stimulated insulin secretion
      target: pathophysiology#Beta Cell Dysfunction
      description: Dynamic insulin secretion normalized to beta-cell abundance.
      biological_processes:
      - preferred_term: insulin secretion
        term:
          id: GO:0030073
          label: insulin secretion
      assays:
      - preferred_term: glucose-stimulated insulin secretion assay
      direction: NEGATIVE
    - name: Beta-cell identity and alpha-like trajectory markers
      target: pathophysiology#Beta Cell Dysfunction
      description: >-
        Single-cell expression of INS, MAFA, INSM1, NPY, ALDH1A3, GCG, and
        SMOC1 interpreted as mature beta-cell identity versus dedifferentiated
        or alpha-like trajectory state.
      assays:
      - preferred_term: single-cell transcriptomic profiling
      - preferred_term: immunofluorescence assay
      direction: POSITIVE
    - name: Adipocyte-associated immune recruitment
      target: pathophysiology#Insulin Resistance
      description: T-cell proximity and inflammatory-cytokine readouts around adipocyte-islet interfaces.
      biological_processes:
      - preferred_term: inflammatory response
        term:
          id: GO:0006954
          label: inflammatory response
      assays:
      - preferred_term: multiplex cytokine profiling
      - preferred_term: spatial transcriptomic profiling
      direction: POSITIVE
    controls:
    - name: Islet-only culture
      description: Donor-matched islets cultured without adipocytes or immune cells.
    - name: Non-diabetic donor islet-adipocyte coculture
      description: Parallel coculture built from non-diabetic donor material.
    - name: Vehicle-treated stressed coculture
      description: Stressed coculture receiving vehicle instead of incretin or SMOC1 perturbation.
    decision_criterion: >-
      Dedifferentiation is supported as a reversible causal mechanism if
      adipocyte-inflammatory challenge induces beta-cell identity loss and
      impaired insulin secretion, and if SMOC1 suppression or incretin rescue
      restores mature beta-cell markers and insulin secretion before cell-loss
      dominates.
    would_support:
    - pathophysiology#Beta Cell Dysfunction
    - pathophysiology#Incretin Axis Dysfunction
    evidence:
    - reference: PMID:40072535
      reference_title: "Intrapancreatic adipocytes and beta cell dedifferentiation in human type 2 diabetes."
      supports: SUPPORT
      evidence_source: HUMAN_CLINICAL
      snippet: "Higher pancreatic fat content was accompanied by increased beta cell dedifferentiation in the individuals with diabetes."
      explanation: >-
        Supports the human association between pancreatic adiposity and
        beta-cell dedifferentiation that this experiment tries to make causal
        and reversible.
    - reference: PMID:40072535
      reference_title: "Intrapancreatic adipocytes and beta cell dedifferentiation in human type 2 diabetes."
      supports: SUPPORT
      evidence_source: HUMAN_CLINICAL
      snippet: "the interactions among adipocytes, immune cells and beta cells in the pancreas microenvironment might contribute to beta cell failure and dedifferentiation"
      explanation: >-
        Motivates modeling the islet-adipocyte-immune microenvironment rather
        than studying isolated beta cells alone.
    - reference: PMID:41057332
      reference_title: "Human pancreatic α-cell heterogeneity and trajectory inference analyses reveal SMOC1 as a β-cell dedifferentiation gene."
      supports: SUPPORT
      evidence_source: IN_VITRO
      snippet: "Enhanced SMOC1 expression in β-cells decreased insulin expression and secretion and increased β-cell dedifferentiation markers."
      explanation: >-
        Provides a perturbational handle for testing whether the alpha-like
        trajectory is mechanistically upstream of beta-cell dysfunction.
datasets:
# Type 2 Diabetes Gut Microbiome Studies

- accession: bioproject:PRJNA361402
  title: Metformin treatment effects on gut microbiome in T2D
  description: >-
    Shotgun metagenomics from 40 individuals in a randomized, placebo-controlled,
    double-blind type 2 diabetes study. Samples at baseline and after 4 months
    of metformin treatment to assess drug-microbiome interactions.
  organism:
    preferred_term: human gut metagenome
    term:
      id: NCBITaxon:408170
      label: human gut metagenome
  data_type: WGS
  sample_types:
  - preferred_term: fecal sample
    term:
      id: UBERON:0001988
      label: feces
    tissue_term:
      preferred_term: feces
      term:
        id: UBERON:0001988
        label: feces
  sample_count: 40
  conditions:
  - type 2 diabetes metformin treatment
  - type 2 diabetes placebo
  notes: Nature Communications 2022 - metformin-microbiome interactions

- accession: bioproject:PRJNA607849
  title: Gut microbiome in urban African type 2 diabetes
  description: >-
    16S rRNA gene sequencing of gut microbiome profiles from type 2 diabetes
    patients and controls in urban African populations, examining geographic
    and dietary influences on diabetes-associated microbiome signatures.
  organism:
    preferred_term: human gut metagenome
    term:
      id: NCBITaxon:408170
      label: human gut metagenome
  sample_types:
  - preferred_term: fecal sample
    term:
      id: UBERON:0001988
      label: feces
    tissue_term:
      preferred_term: feces
      term:
        id: UBERON:0001988
        label: feces
  conditions:
  - type 2 diabetes
  - healthy controls
  notes: Frontiers Cellular Infection Microbiology 2020

- accession: bioproject:PRJNA554535
  title: Gut microbiota in obese T2DM patients - Pakistani cohort
  description: >-
    16S rRNA sequencing of gut microbiota from 60 Pakistani adults comparing
    obese individuals with type 2 diabetes to healthy controls. V3-V4
    hypervariable regions sequenced.
  organism:
    preferred_term: human gut metagenome
    term:
      id: NCBITaxon:408170
      label: human gut metagenome
  sample_types:
  - preferred_term: fecal sample
    term:
      id: UBERON:0001988
      label: feces
    tissue_term:
      preferred_term: feces
      term:
        id: UBERON:0001988
        label: feces
  sample_count: 60
  conditions:
  - obese type 2 diabetes
  - healthy controls
  publication: PMID:31809500

- accession: bioproject:PRJNA422434
  title: Chinese MGWAS of gut microbiome in type 2 diabetes
  description: >-
    Landmark metagenome-wide association study (MGWAS) comparing gut microbial
    DNA from 345 Chinese individuals. Identified ~60,000 T2D-associated markers
    and established metagenomic linkage groups.
  organism:
    preferred_term: human gut metagenome
    term:
      id: NCBITaxon:408170
      label: human gut metagenome
  data_type: WGS
  sample_types:
  - preferred_term: fecal sample
    term:
      id: UBERON:0001988
      label: feces
    tissue_term:
      preferred_term: feces
      term:
        id: UBERON:0001988
        label: feces
  sample_count: 345
  conditions:
  - type 2 diabetes
  - healthy controls
  publication: PMID:23023125
  notes: Nature 2012 - first MGWAS of T2D, foundational study
computational_models:
- name: Topp Beta-Cell Mass / Insulin / Glucose Model
  description: >-
    Minimal three-ODE dynamical model of the glucose regulatory system (Topp et
    al. 2000): plasma glucose, plasma insulin, and beta-cell mass, with fast
    glucose/insulin dynamics on a slow beta-cell-mass manifold. For normal
    parameters the system is bistable - a physiological steady state (euglycemia)
    and a pathological, insulinopenic steady state (beta-cell-mass collapse and
    severe hyperglycemia) separated by a saddle. This makes it the reference
    dynamical substrate for type 2 diabetes: reduced insulin sensitivity,
    impaired secretion, or hepatic glucose overproduction decompensates the
    at-risk state to overt diabetes, and it is wired for perturbation analysis
    (see the model config sidecar) so that both risk genes and glucose-lowering
    treatments can be simulated as parameter changes.
  model_type: KINETIC
  repository_url: https://www.ebi.ac.uk/biomodels/BIOMD0000000341
  model_id: BIOMD0000000341
  model_software: COPASI
  model_format: SBML
  publication: PMID:11013117
  perturbations:
  - preferred_term: PPARG
    term:
      id: hgnc:9236
      label: PPARG
  - preferred_term: TCF7L2
    term:
      id: hgnc:11641
      label: TCF7L2
  - preferred_term: KCNJ11
    term:
      id: hgnc:6257
      label: KCNJ11
  - preferred_term: HNF1A
    term:
      id: hgnc:11621
      label: HNF1A
  - preferred_term: GCK
    term:
      id: hgnc:4195
      label: GCK
  - preferred_term: SLC5A2
    term:
      id: hgnc:11037
      label: SLC5A2
  modeled_mechanisms:
  - target: Insulin Resistance
    description: >-
      Reduced whole-body insulin sensitivity is the model parameter si; lowering
      si (or PPARG/thiazolidinedione perturbation of si) reproduces the
      insulin-resistance driver of type 2 diabetes.
  - target: Beta Cell Dysfunction
    description: >-
      Maximal per-beta-cell secretory capacity (sigma) and the beta-cell-mass
      state variable (B) capture secretory failure and glucotoxic beta-cell-mass
      loss; TCF7L2/KCNJ11/HNF1A perturbations act here.
  - target: Hepatic Glucose Overproduction
    description: >-
      Insulin-independent hepatic glucose output is the parameter R0, the target
      reduced by metformin in the simulated treatment scenarios.
  evidence:
  - reference: PMID:11013117
    reference_title: "A model of beta-cell mass, insulin, and glucose kinetics: pathways to diabetes."
    supports: SUPPORT
    evidence_source: COMPUTATIONAL
    snippet: >-
      which consists of a system of three nonlinear ordinary differential
      equations, where glucose and insulin dynamics are fast relative to
      beta-cell mass dynamics
    explanation: >-
      Establishes the Topp model as a three-ODE dynamical model of beta-cell
      mass, insulin, and glucose - the substrate used here for simulating type 2
      diabetes mechanisms and treatments.
  findings:
  - statement: >-
      For normal parameters the model is bistable, with a physiological
      (euglycemic) and a pathological (hyperglycemic, beta-cell-collapse) steady
      state separated by a saddle - the dynamical basis for decompensation to
      overt diabetes.
    evidence:
    - reference: PMID:11013117
      reference_title: "A model of beta-cell mass, insulin, and glucose kinetics: pathways to diabetes."
      supports: SUPPORT
      evidence_source: COMPUTATIONAL
      snippet: >-
        For normal parameter values, the model has two stable fixed points
        (representing physiological and pathological steady states), separated on
        a slow manifold by a saddle point
      explanation: >-
        Confirms the bistable, saddle-separated structure that lets an impairing
        perturbation tip the at-risk state to overt diabetes while a corrective
        treatment restores euglycemia.
  - statement: >-
      The model defines three routes to prolonged hyperglycemia (regulated
      hyperglycemia, saddle-node bifurcation, and dynamical hyperglycemia),
      mapping onto insulin-resistance, secretory-failure, and hepatic-output
      drivers of type 2 diabetes.
    evidence:
    - reference: PMID:11013117
      reference_title: "A model of beta-cell mass, insulin, and glucose kinetics: pathways to diabetes."
      supports: SUPPORT
      evidence_source: COMPUTATIONAL
      snippet: >-
        The model predicts that there are three pathways in prolonged
        hyperglycemia
      explanation: >-
        Grounds the perturbation scenarios: each disease driver corresponds to
        one of the model's predicted pathways into sustained hyperglycemia.
  notes: >-
    Wired for dismech-perturb (models/BIOMD0000000341.config.yaml). The
    disease-severity dial is insulin sensitivity si (baseline_gfr 0.72 = healthy);
    the deposited initial state (G=250 mg/dL) sits on the model's unstable saddle,
    i.e. the metabolically at-risk / impaired-fasting tipping point.
    Glucose-lowering treatments are simulated as parameter changes: metformin
    (R0 down),
    thiazolidinedione (si up), SGLT2 inhibitor (Eg0 up, insulin-independent),
    sulfonylurea/GLP-1 (sigma up), insulin therapy (net insulin action up).
    Insulin-independent therapies (SGLT2 inhibition, metformin) and sensitizers
    (TZD) recompensate the model to euglycemia, whereas a pure secretagogue fails
    once beta-cell mass has collapsed - reproducing secondary secretagogue
    failure in advanced disease. Thresholds are calibrated to model steady-state
    values, not clinical reference ranges.
  variables:
  - name: Fasting_Plasma_Glucose
    dataset_identifier: G
    description: Plasma glucose concentration at steady state.
    unit: mg/dL
    mappings_list:
    - preferred_term: glucose
      term:
        id: CHEBI:17234
        label: glucose
    - preferred_term: Hyperglycemia
      description: >-
        Steady-state plasma glucose above the model's decompensation level. The
        pathological fixed point runs near 600 mg/dL; the physiological fixed
        point near 100 mg/dL. Bands are calibrated to model steady states, not
        clinical fasting-glucose cutoffs.
      term:
        id: HP:0003074
        label: Hyperglycemia
      threshold: 126
      threshold_direction: above
      severity_scale:
      - threshold: 126
        name: mild
      - threshold: 200
        name: moderate
      - threshold: 400
        name: severe
  - name: Plasma_Insulin
    dataset_identifier: I
    description: >-
      Plasma insulin concentration at steady state. Elevated in the compensated
      insulin-resistant regime and near-zero after glucotoxic beta-cell-mass
      collapse.
    unit: uU/mL
    mappings_list:
    - preferred_term: Hyperinsulinemia
      description: >-
        Compensatory hyperinsulinemia in the insulin-resistant regime before
        beta-cell-mass collapse. Threshold calibrated to model steady state
        (healthy ~10 uU/mL).
      term:
        id: HP:0000842
        label: Hyperinsulinemia
      threshold: 18
      threshold_direction: above
      severity_scale:
      - threshold: 18
        name: mild
      - threshold: 30
        name: moderate
      - threshold: 60
        name: severe
  - name: Beta_Cell_Mass
    dataset_identifier: B
    description: >-
      Functional beta-cell mass (slow state variable). Expands under mild
      hyperglycemia (compensation) and collapses toward zero under sustained
      extreme hyperglycemia (glucotoxicity), the positive-feedback route to
      insulinopenic diabetes.
    unit: mg
- name: Pancreatic Beta Cell Genome-Scale Metabolic Model
  description: >
    First comprehensive genome-scale metabolic reconstruction of human pancreatic
    beta cells,
    integrating transcriptomic data from healthy and type 2 diabetic islets. The model
    captures
    beta cell-specific metabolic pathways and identifies metabolic alterations in
    T2D including
    impaired glucose-stimulated insulin secretion mechanisms.
  model_type: GENOME_SCALE_METABOLIC
  publication: PMID:35276551
  notes: PLOS Computational Biology 2022 - context-specific reconstruction using RNA-seq from healthy and T2D beta cells
- name: Whole-Body Human Metabolic Model for Diabetes
  description: >
    Multi-organ metabolic model (Harvey/Harvetta) capturing inter-organ metabolic
    fluxes in
    diabetes. Models liver, muscle, adipose, and pancreas metabolism with tissue-specific
    constraints derived from omics data.
  model_type: GENOME_SCALE_METABOLIC
  base_model: Recon3D
  repository_url: https://www.vmh.life/
  publication: PMID:32472720
  notes: Predicts diabetes biomarkers and drug effects across multiple organs
- name: PBPK Model for GLP-1 Receptor Agonists
  description: >
    Physiologically-based pharmacokinetic model for GLP-1 receptor agonists (semaglutide,
    tirzepatide) in T2D patients. Incorporates drug absorption, distribution, and
    receptor
    binding kinetics to optimize dosing regimens.
  model_type: PHYSIOLOGICAL
  notes: Used in clinical trial design and dose optimization for incretin-based therapies
- name: AGORA2 Gut Microbiome Metabolic Models
  description: >
    Collection of 7,302 strain-resolved genome-scale metabolic reconstructions of
    human
    gut microorganisms. Enables personalized microbiome-host metabolic modeling by
    integrating with human metabolic models (Recon3D). Captures strain-level variation
    in SCFA production, bile acid metabolism, and drug biotransformation relevant
    to T2D.
  model_type: GENOME_SCALE_METABOLIC
  repository_url: https://www.vmh.life/
  publication: PMID:36543475
  notes: Nature Biotechnology 2022 - includes drug metabolism capabilities for 98 drugs; enables community-level FBA with MICOM
- name: MICOM Community Metabolic Model
  description: >
    Metagenome-scale modeling framework for simulating metabolic interactions in the
    gut microbiota. Integrates dietary constraints and taxon abundances from metagenomic
    data to predict personalized SCFA production, cross-feeding networks, and metabolic
    fluxes. Applied to T2D to study dysbiosis effects on butyrate production and
    glucose-insulin signaling.
  model_type: GENOME_SCALE_METABOLIC
  model_software: COBRApy
  publication: PMID:31964767
  notes: mSystems 2020 - enables personalized microbiome metabolic modeling from 16S/metagenomics data
references:
- reference: DOI:10.1007/s00592-024-02300-6
  title: 'GLP1-GIP receptor co-agonists: a promising evolution in the treatment of type 2 diabetes'
  findings: []
- reference: DOI:10.1007/s43152-024-00056-3
  title: Cellular and Molecular Mechanisms of Insulin Resistance
  findings: []
- reference: DOI:10.1038/s42255-024-01140-6
  title: Genetic architecture of oral glucose-stimulated insulin release provides biological insights into type 2 diabetes aetiology
  findings: []
- reference: DOI:10.3390/ijms25031504
  title: Mitochondrial Dysfunction, Oxidative Stress, and Inter-Organ Miscommunications in T2D Progression
  findings: []
- reference: DOI:10.3390/ijms26031094
  title: 'Type 2 Diabetes Mellitus: New Pathogenetic Mechanisms, Treatment and the Most Important Complications'
  findings: []
- reference: DOI:10.3390/nu17162708
  title: Type 2 Diabetes and the Multifaceted Gut-X Axes
  findings: []
📚

References & Deep Research

References

6
DOI:10.1007/s00592-024-02300-6
GLP1-GIP receptor co-agonists: a promising evolution in the treatment of type 2 diabetes
No top-level findings curated for this source.
DOI:10.1007/s43152-024-00056-3
Cellular and Molecular Mechanisms of Insulin Resistance
No top-level findings curated for this source.
DOI:10.1038/s42255-024-01140-6
Genetic architecture of oral glucose-stimulated insulin release provides biological insights into type 2 diabetes aetiology
No top-level findings curated for this source.
DOI:10.3390/ijms25031504
Mitochondrial Dysfunction, Oxidative Stress, and Inter-Organ Miscommunications in T2D Progression
No top-level findings curated for this source.
DOI:10.3390/ijms26031094
Type 2 Diabetes Mellitus: New Pathogenetic Mechanisms, Treatment and the Most Important Complications
No top-level findings curated for this source.
DOI:10.3390/nu17162708
Type 2 Diabetes and the Multifaceted Gut-X Axes
No top-level findings curated for this source.

Deep Research

2
Disorder ▸

Disorder

  • Name: Type 2 Diabetes Mellitus
  • Category: Complex
  • Existing deep-research providers: falcon
  • Existing evidence reference count in YAML: 29

Key Pathophysiology Nodes

  • Insulin Resistance
  • Beta Cell Dysfunction
  • Hepatic Glucose Overproduction
  • Mitochondrial Dysfunction and Oxidative Stress
  • Incretin Axis Dysfunction
  • Deep research literature mapping

Citation Inventory (for evidence mapping)

  • DOI:10.1007/s00592-024-02300-6
  • DOI:10.1007/s43152-024-00056-3
  • DOI:10.1038/s42255-024-01140-6
  • DOI:10.3390/ijms25031504
  • DOI:10.3390/ijms26031094
  • DOI:10.3390/nu17162708
Falcon ▸
Disease Pathophysiology Research Report
Edison Scientific Literature 16 citations 2025-12-17T18:38:21.178177

Disease Pathophysiology Research Report

Target Disease - Disease Name: Type 2 Diabetes Mellitus (T2DM) - MONDO ID: MONDO:0005148 - Category: Complex

Pathophysiology description (current understanding, 2023–2024 focus) T2DM arises from the convergence of peripheral insulin resistance (IR) in liver, skeletal muscle, and adipose tissue with progressive pancreatic β-cell dysfunction. IR is driven by intracellular lipid intermediates (diacylglycerols, ceramides) that activate protein kinase C isoforms and promote inhibitory serine phosphorylation of IRS proteins, blunting PI3K–AKT signaling in insulin-responsive tissues, together with inflammation, adipokine imbalance, ER stress, and mitochondrial dysfunction (reviewed mechanistically by Chandrasekaran & Weiskirchen 2024; journal page provides overview of INSR/IRS/PI3K/AKT and mTOR/S6K feedback nodes; https://doi.org/10.1007/s43152-024-00056-3, Feb 2024) (chandrasekaran2024cellularandmolecular pages 1-2). β-cell failure reflects glucolipotoxic stress that perturbs ER proteostasis (UPR activation and proinsulin misfolding), damages mitochondria, alters redox signaling, and can culminate in identity loss/dedifferentiation; islet amyloid polypeptide (IAPP) deposition correlates with β-cell loss in human T2DM (review synthesis 2025 with 2023–2024 literature integration; https://doi.org/10.3390/ijms26031094) (młynarska2025type2diabetes pages 16-18). Cross-tissue mitochondrial dysfunction and pathological ROS occur early in muscle, adipose, and islets and propagate via extracellular vesicle–mediated signals, disturbing mitophagy and organelle dynamics (https://doi.org/10.3390/ijms25031504, Jan 2024) (veluthakal2024mitochondrialdysfunctionoxidative pages 1-2).

Incretin biology is central to gut–islet crosstalk: GLP-1 action is relatively preserved whereas GIP action is often blunted in T2DM; dual GLP-1/GIP agonism (e.g., tirzepatide) leverages cAMP–PKA signaling to amplify glucose-dependent insulin secretion, enhance β-cell survival, and improve weight and cardiometabolic endpoints (Acta Diabetologica 2024; https://doi.org/10.1007/s00592-024-02300-6, Jun 2024) (ciardullo2024glp1gipreceptorcoagonists pages 1-2). Large-scale human genetics and integrative omics now map effector genes and pathways controlling β-cell function during oral glucose challenges; a 2024 Nature Metabolism GWAS meta-analysis of multiple OGTT-derived β-cell indices identified 55 signals at 44 loci and nominated effector genes (e.g., ACSL1, FAM46C) that modulate insulin secretion in β-cell models (https://doi.org/10.1038/s42255-024-01140-6, Oct 2024) (madsen2024geneticarchitectureof pages 1-2). The gut–liver–pancreas axis (dysbiosis, permeability, LPS/TLR4 activation; SCFA and bile-acid signaling) contributes to systemic inflammation, IR, and β-cell stress, providing mechanistic rationale for microbiome-targeted strategies (Nutrients 2025 synthesis of 2015–2024 evidence; https://doi.org/10.3390/nu17162708) (guo2025type2diabetes pages 4-5).

Mechanism Key molecules/genes (HGNC) Cell types (CL IDs) Tissues (UBERON IDs) Representative GO processes/components Supporting evidence (context IDs)
Peripheral insulin resistance: lipid intermediates (DAG/ceramides) → PKC activation; IRS serine phosphorylation impairing PI3K/AKT signaling PRKCQ, IRS1; lipid intermediates: DAG, CER Skeletal muscle cell (CL:0000187); Adipocyte (CL:0000136); Hepatocyte (CL:0000182) Skeletal muscle organ (UBERON:0002370); Adipose tissue (UBERON:0000990); Liver (UBERON:0002107) insulin receptor signaling pathway (GO:0008286); protein kinase C signaling (GO:0070528) (chandrasekaran2024cellularandmolecular pages 1-2, młynarska2025type2diabetes pages 16-18)
Mitochondrial dysfunction & oxidative stress with mitophagy and EV-mediated inter-organ crosstalk PINK1, PRKN, SOD2; mitophagy regulators Pancreatic beta cell (CL:0000169); Skeletal muscle cell (CL:0000187) Pancreas (UBERON:0001264); Skeletal muscle organ (UBERON:0002370) mitophagy (GO:0000422); response to oxidative stress (GO:0006979) (veluthakal2024mitochondrialdysfunctionoxidative pages 1-2, młynarska2025type2diabetes pages 16-18)
Incretin axis — GLP-1/GIP signaling and therapeutic co-agonism influencing β-cell function and systemic metabolism GLP1R, GIPR Pancreatic beta cell (CL:0000169); Enterocyte/enteroendocrine lineage (CL:0000584) Pancreas (UBERON:0001264); Small intestine (UBERON:0002108) cAMP-mediated signaling (GO:0019933); regulation of insulin secretion (GO:0050796) (ciardullo2024glp1gipreceptorcoagonists pages 1-2, chandrasekaran2024cellularandmolecular pages 1-2)
β-cell genetic effector genes (OGTT-based GWAS) that modulate insulin secretion ACSL1, FAM46C (candidate effector genes from BCF-GWAS) Pancreatic beta cell (CL:0000169) Pancreas (UBERON:0001264) regulation of insulin secretion (GO:0050796); insulin receptor signaling (GO:0008286) (madsen2024geneticarchitectureof pages 1-2, młynarska2025type2diabetes pages 16-18)
Gut–liver–pancreas crosstalk: microbiome metabolites and endotoxin-driven inflammation (SCFAs, bile acids, LPS → TLR4) affecting IR and β-cell health TLR4, FFAR2, FFAR3 Enterocyte (CL:0000584); Hepatocyte (CL:0000182); Pancreatic beta cell (CL:0000169) Small intestine (UBERON:0002108); Liver (UBERON:0002107); Pancreas (UBERON:0001264) LPS-mediated TLR4 signaling (GO:0034142); bile acid receptor signaling pathway (GO:1902653) (guo2025type2diabetes pages 4-5, chandrasekaran2024cellularandmolecular pages 1-2)

Table: Compact ontology-aligned summary mapping five core T2D mechanisms to key genes, cell/tissue ontology IDs, representative GO terms, and supporting contemporary evidence for use in knowledge-base annotation.

Core Pathophysiology - Primary mechanisms - Peripheral insulin resistance via lipid-driven PKC activation and impaired INSR–IRS–PI3K–AKT signaling in muscle, adipose, and liver; aggravated by inflammatory and ER-stress signaling and mTOR/S6K negative feedback (https://doi.org/10.1007/s43152-024-00056-3) (chandrasekaran2024cellularandmolecular pages 1-2). - β-cell ER stress and UPR activation with proinsulin folding load; mitochondrial dysfunction/oxidative stress; progressive loss of β-cell identity; IAPP deposition correlating with β-cell loss (https://doi.org/10.3390/ijms26031094) (młynarska2025type2diabetes pages 16-18). - Early, tissue-spanning mitochondrial dysfunction and ROS that drive inter-organ miscommunication (EVs, disturbed mitophagy) (https://doi.org/10.3390/ijms25031504) (veluthakal2024mitochondrialdysfunctionoxidative pages 1-2). - Incretin axis attenuation and pharmacologic rescue with GLP-1/GIP agonism (https://doi.org/10.1007/s00592-024-02300-6) (ciardullo2024glp1gipreceptorcoagonists pages 1-2). - Gut–liver–pancreas crosstalk: endotoxemia (LPS→TLR4), altered SCFAs and bile-acid signaling impair insulin sensitivity and β-cell function (https://doi.org/10.3390/nu17162708) (guo2025type2diabetes pages 4-5).

  • Dysregulated molecular pathways and affected cellular processes
  • Insulin receptor signaling pathway; PKC activation by DAG/ceramides; IRS serine phosphorylation; downstream GLUT4 trafficking defects (https://doi.org/10.1007/s43152-024-00056-3) (chandrasekaran2024cellularandmolecular pages 1-2).
  • ER stress/UPR arms (PERK–eIF2α/ATF4, IRE1–XBP1s, ATF6); proinsulin proteostasis strain; β-cell identity programs (review integration) (https://doi.org/10.3390/ijms26031094) (młynarska2025type2diabetes pages 16-18).
  • Mitochondrial ROS production, impaired mitophagy (PINK1–PRKN), and organelle dynamics affecting GSIS and survival (https://doi.org/10.3390/ijms25031504) (veluthakal2024mitochondrialdysfunctionoxidative pages 1-2).
  • Incretin receptor (GLP1R, GIPR) cAMP–PKA signaling amplifying GSIS and promoting survival; system-level metabolic actions (https://doi.org/10.1007/s00592-024-02300-6) (ciardullo2024glp1gipreceptorcoagonists pages 1-2).
  • TLR4/NF-κB activation by LPS; reduced SCFA receptor (FFAR2/3) signaling; altered bile-acid (FXR/TGR5) pathways (https://doi.org/10.3390/nu17162708) (guo2025type2diabetes pages 4-5).

Key Molecular Players - Genes/Proteins (HGNC; examples) - INSR, IRS1/2, PIK3R1/PIK3CA, AKT2; PRKCQ (PKC-θ); mTOR/S6K1 (https://doi.org/10.1007/s43152-024-00056-3) (chandrasekaran2024cellularandmolecular pages 1-2). - ER stress/UPR: EIF2AK3 (PERK), ERN1 (IRE1), ATF6; chaperones HSPA5 (BiP/GRP78); TXNIP as stress amplifier (review) (https://doi.org/10.3390/ijms26031094) (młynarska2025type2diabetes pages 16-18). - Mitochondria/mitophagy: PINK1, PRKN; antioxidant SOD2 (https://doi.org/10.3390/ijms25031504) (veluthakal2024mitochondrialdysfunctionoxidative pages 1-2). - Incretin axis: GLP1R, GIPR (https://doi.org/10.1007/s00592-024-02300-6) (ciardullo2024glp1gipreceptorcoagonists pages 1-2). - β-cell effector genes: ACSL1, FAM46C from OGTT-based BCF GWAS; functional silencing impacts insulin secretion (https://doi.org/10.1038/s42255-024-01140-6) (madsen2024geneticarchitectureof pages 1-2). - Innate/metabolic sensors: TLR4; SCFA receptors FFAR2/FFAR3 (https://doi.org/10.3390/nu17162708) (guo2025type2diabetes pages 4-5).

  • Chemical entities (CHEBI; examples)

  • Diacylglycerols (DAG), ceramides (CER) as lipotoxic mediators; lipopolysaccharide (LPS); SCFAs (acetate, butyrate); bile acids (class) (mechanisms summarized) (chandrasekaran2024cellularandmolecular pages 1-2, guo2025type2diabetes pages 4-5).

  • Cell types (CL)

  • Skeletal myocyte (CL:0000187), adipocyte (CL:0000136), hepatocyte (CL:0000182), pancreatic β-cell (CL:0000169), enterocyte/enteroendocrine lineage (CL:0000584) (mechanistic mapping) (chandrasekaran2024cellularandmolecular pages 1-2, ciardullo2024glp1gipreceptorcoagonists pages 1-2, guo2025type2diabetes pages 4-5).

  • Anatomical locations (UBERON)

  • Skeletal muscle organ (UBERON:0002370), adipose tissue (UBERON:0000990), liver (UBERON:0002107), pancreas (UBERON:0001264), small intestine (UBERON:0002108) (chandrasekaran2024cellularandmolecular pages 1-2, ciardullo2024glp1gipreceptorcoagonists pages 1-2, guo2025type2diabetes pages 4-5).

Biological Processes (for GO annotation; examples) - Insulin receptor signaling pathway (GO:0008286) and GLUT4 trafficking defects in IR tissues (mechanism synthesis) (chandrasekaran2024cellularandmolecular pages 1-2). - Protein kinase C signaling (GO:0070528) downstream of DAG/ceramide (chandrasekaran2024cellularandmolecular pages 1-2). - Unfolded protein response (GO:0030968) and ER stress signaling in β-cells (młynarska2025type2diabetes pages 16-18). - Mitophagy (GO:0000422) and response to oxidative stress (GO:0006979) across islets and muscle (veluthakal2024mitochondrialdysfunctionoxidative pages 1-2). - cAMP-mediated signaling (GO:0019933) and regulation of insulin secretion (GO:0050796) by GLP-1/GIP (ciardullo2024glp1gipreceptorcoagonists pages 1-2). - LPS-mediated TLR4 signaling (GO:0034142) and bile-acid receptor signaling pathway (GO:1902653) in gut–liver axis (guo2025type2diabetes pages 4-5).

Cellular Components (where processes occur) - Plasma membrane/lipid rafts: INSR/IRS complex; PKC localization in IR (chandrasekaran2024cellularandmolecular pages 1-2). - Endoplasmic reticulum: proinsulin folding, UPR sensors PERK/IRE1/ATF6 (młynarska2025type2diabetes pages 16-18). - Mitochondria: respiratory chain, ROS generation, mitophagy machinery (PINK1/PRKN) (veluthakal2024mitochondrialdysfunctionoxidative pages 1-2). - Endosomes/secretory granules: incretin receptor trafficking; insulin granule exocytosis (ciardullo2024glp1gipreceptorcoagonists pages 1-2). - TLR4 localization at the plasma membrane of enterocytes/hepatocytes/immune cells (guo2025type2diabetes pages 4-5).

Disease Progression (sequence of events) 1) Energy surplus, inactivity, and/or genetic risk promote ectopic lipid accumulation in muscle/liver/adipose; DAG/ceramides activate PKC and inhibit INSR–IRS–PI3K–AKT, producing tissue-specific IR with compensatory hyperinsulinemia (https://doi.org/10.1007/s43152-024-00056-3) (chandrasekaran2024cellularandmolecular pages 1-2). 2) Chronic nutrient load and inflammatory signaling induce ER stress/UPR and mitochondrial dysfunction in β-cells; redox imbalance and defective mitophagy accumulate, impairing GSIS (https://doi.org/10.3390/ijms25031504; https://doi.org/10.3390/ijms26031094) (veluthakal2024mitochondrialdysfunctionoxidative pages 1-2, młynarska2025type2diabetes pages 16-18). 3) Progressive β-cell dysfunction (reduced function/mass, dedifferentiation) fails to match IR, unmasking fasting and postprandial hyperglycemia (review synthesis; clinical trajectory) (młynarska2025type2diabetes pages 16-18). 4) Gut dysbiosis and barrier defects worsen systemic inflammation (LPS→TLR4) and metabolic signaling (reduced SCFA and altered bile-acid signaling), aggravating IR and β-cell stress (https://doi.org/10.3390/nu17162708) (guo2025type2diabetes pages 4-5). 5) Incretin defect (blunted GIP) diminishes oral-glucose insulinotropic effect; pharmacologic GLP-1/GIP agonism can restore gut–islet amplification, reduce weight, and improve cardiometabolic profiles (https://doi.org/10.1007/s00592-024-02300-6) (ciardullo2024glp1gipreceptorcoagonists pages 1-2).

Phenotypic Manifestations (selected HP terms) - Hyperglycemia (HP:0003074), Impaired glucose tolerance (HP:0001952), Insulin resistance (HP:0000855), Hyperinsulinemia (HP:0000846), Obesity (HP:0001513). Mechanistically linked to IR and β-cell dysfunction as above (chandrasekaran2024cellularandmolecular pages 1-2, młynarska2025type2diabetes pages 16-18).

Recent developments and latest research (2023–2024 priority) - Archetypes and early β-cell failure: Evidence that lean T2D can present with early β-cell dysfunction without marked IR; across tissues, pathological ROS and mitochondrial dysfunction emerge early and may drive progression; EV-mediated organ crosstalk is implicated (IJMS 2024; https://doi.org/10.3390/ijms25031504) (veluthakal2024mitochondrialdysfunctionoxidative pages 1-2). - OGTT-based β-cell function genetics: GWAS meta-analysis of eight OGTT-derived β-cell traits identified 55 signals/44 loci and 92 candidate effectors; ACSL1 and FAM46C validated as regulators of insulin secretion in human β-cell models (Nature Metabolism 2024; https://doi.org/10.1038/s42255-024-01140-6) (madsen2024geneticarchitectureof pages 1-2). - Incretin co-agonism: Dual GLP-1/GIP agonists (tirzepatide) show strong glucose-lowering and weight loss with mechanistic actions on β-cell mass/function and multiorgan metabolism (Acta Diabetologica 2024; https://doi.org/10.1007/s00592-024-02300-6) (ciardullo2024glp1gipreceptorcoagonists pages 1-2). - Systems synthesis of IR mechanisms: Consolidated molecular map of INSR/IRS/PI3K/AKT with lipid-induced PKC activation, mTOR/S6K negative feedback, ER stress, and mitochondrial dysfunction as convergent IR pathways (2024 review; https://doi.org/10.1007/s43152-024-00056-3) (chandrasekaran2024cellularandmolecular pages 1-2). - Gut–X axes: Integrative review outlines gut permeability/endotoxemia, BCAA/SCFA/bile-acid signaling, and neural–endocrine crosstalk linking microbiota to hepatic IR and β-cell function (Nutrients 2025; covers 2015–2024 human/animal data; https://doi.org/10.3390/nu17162708) (guo2025type2diabetes pages 4-5).

Current applications and real-world implementations - Incretin-based therapeutics: GLP-1 receptor agonists and GLP-1/GIP co-agonists used for T2DM and obesity produce glucose-dependent insulinotropic effects, appetite suppression, and cardiometabolic benefit; mechanisms via cAMP–PKA amplifying β-cell exocytosis, anti-apoptosis, and multiorgan actions (https://doi.org/10.1007/s00592-024-02300-6) (ciardullo2024glp1gipreceptorcoagonists pages 1-2). - Mechanism-driven stratification: Genetic variation at GLP1R and β-cell effector genes (e.g., ACSL1, FAM46C) suggests avenues for precision incretin therapy and β-cell–centric target discovery (https://doi.org/10.1038/s42255-024-01140-6) (madsen2024geneticarchitectureof pages 1-2). - Systems targets for IR: Strategies reducing DAG/ceramide load, dampening mTOR/S6K feedback, or alleviating ER/mitochondrial stress align to the consolidated IR map (https://doi.org/10.1007/s43152-024-00056-3) (chandrasekaran2024cellularandmolecular pages 1-2).

Expert opinions and authoritative analysis - Mechanistic IR framework (INSR–IRS–PI3K–AKT; lipid–PKC; mTOR/S6K feedback) remains the cornerstone for interpreting tissue-specific IR and for target selection (Chandrasekaran & Weiskirchen 2024) (chandrasekaran2024cellularandmolecular pages 1-2). - Contemporary view emphasizes mitochondrial dysfunction/ROS and inter-organ miscommunication as early, unifying drivers across patient archetypes, refocusing prevention on mitochondrial quality control and oxidative signaling (Veluthakal et al., 2024) (veluthakal2024mitochondrialdysfunctionoxidative pages 1-2). - Incretin co-agonism represents a mechanistically coherent platform therapy addressing both β-cell and whole-body energy balance (Ciardullo et al., 2024) (ciardullo2024glp1gipreceptorcoagonists pages 1-2).

Relevant statistics and data - OGTT β-cell function GWAS: 55 independent associations at 44 loci across eight β-cell indices (~26,000 individuals), nominating 92 candidate effector genes; ACSL1/FAM46C perturbation alters insulin secretion in β-cell models (Nature Metabolism 2024) (madsen2024geneticarchitectureof pages 1-2). - Prediabetes global burden: ~541 million adults; mitochondrial dysfunction and ROS proposed as early drivers (IJMS 2024 synthesis) (veluthakal2024mitochondrialdysfunctionoxidative pages 1-2).

Ontology-aligned annotations - HGNC: INSR; IRS1/IRS2; PIK3CA/PIK3R1; AKT2; PRKCQ; EIF2AK3 (PERK); ERN1 (IRE1); ATF6; HSPA5; PINK1; PRKN; SOD2; GLP1R; GIPR; ACSL1; FAM46C; TLR4; FFAR2/FFAR3 (chandrasekaran2024cellularandmolecular pages 1-2, młynarska2025type2diabetes pages 16-18, veluthakal2024mitochondrialdysfunctionoxidative pages 1-2, ciardullo2024glp1gipreceptorcoagonists pages 1-2, madsen2024geneticarchitectureof pages 1-2, guo2025type2diabetes pages 4-5). - GO Processes: insulin receptor signaling pathway (GO:0008286); protein kinase C signaling (GO:0070528); unfolded protein response (GO:0030968); response to oxidative stress (GO:0006979); mitophagy (GO:0000422); cAMP-mediated signaling (GO:0019933); regulation of insulin secretion (GO:0050796); LPS-mediated TLR4 signaling (GO:0034142); bile acid receptor signaling pathway (GO:1902653) (chandrasekaran2024cellularandmolecular pages 1-2, młynarska2025type2diabetes pages 16-18, veluthakal2024mitochondrialdysfunctionoxidative pages 1-2, ciardullo2024glp1gipreceptorcoagonists pages 1-2, guo2025type2diabetes pages 4-5). - CL: skeletal muscle cell (CL:0000187); adipocyte (CL:0000136); hepatocyte (CL:0000182); pancreatic β-cell (CL:0000169); enterocyte/enteroendocrine (CL:0000584) (mapped in mechanisms) (chandrasekaran2024cellularandmolecular pages 1-2, ciardullo2024glp1gipreceptorcoagonists pages 1-2, guo2025type2diabetes pages 4-5). - UBERON: skeletal muscle organ (UBERON:0002370); adipose tissue (UBERON:0000990); liver (UBERON:0002107); pancreas (UBERON:0001264); small intestine (UBERON:0002108) (chandrasekaran2024cellularandmolecular pages 1-2, ciardullo2024glp1gipreceptorcoagonists pages 1-2, guo2025type2diabetes pages 4-5). - CHEBI: diacylglycerol (DAG); ceramide; lipopolysaccharide (LPS); acetate; butyrate; bile acids (chandrasekaran2024cellularandmolecular pages 1-2, guo2025type2diabetes pages 4-5). - HP: Hyperglycemia (HP:0003074); Impaired glucose tolerance (HP:0001952); Insulin resistance (HP:0000855); Hyperinsulinemia (HP:0000846); Obesity (HP:0001513) (linked in clinical manifestations) (chandrasekaran2024cellularandmolecular pages 1-2, młynarska2025type2diabetes pages 16-18).

Evidence items (with URLs and publication dates) - Chandrasekaran P, Weiskirchen R. Cellular and molecular mechanisms of insulin resistance. Current Tissue Microenvironment Reports. Feb 2024. https://doi.org/10.1007/s43152-024-00056-3 (chandrasekaran2024cellularandmolecular pages 1-2). - Veluthakal R, et al. Mitochondrial dysfunction, oxidative stress, and inter-organ miscommunications in T2D. IJMS. Jan 2024. https://doi.org/10.3390/ijms25031504 (veluthakal2024mitochondrialdysfunctionoxidative pages 1-2). - Madsen AL, et al. Genetic architecture of OGTT β-cell function and effector genes. Nature Metabolism. Oct 2024. https://doi.org/10.1038/s42255-024-01140-6 (madsen2024geneticarchitectureof pages 1-2). - Ciardullo S, et al. GLP-1–GIP receptor co-agonists in T2D. Acta Diabetologica. Jun 2024. https://doi.org/10.1007/s00592-024-02300-6 (ciardullo2024glp1gipreceptorcoagonists pages 1-2). - Guo H, et al. Type 2 diabetes and the multifaceted Gut–X axes. Nutrients. Aug 2025 (synthesizes 2015–2024 research). https://doi.org/10.3390/nu17162708 (guo2025type2diabetes pages 4-5). - Młynarska E, et al. T2DM: new pathogenetic mechanisms. IJMS. Jan 2025 (integrates 2023–2024 findings). https://doi.org/10.3390/ijms26031094 (młynarska2025type2diabetes pages 16-18).

Therapeutic mechanistic implications - Incretin-based therapies (GLP-1 RAs; GLP-1/GIP co-agonists) target cAMP–PKA amplification of GSIS, β-cell survival, gastric emptying, appetite circuits, and renal/cardiovascular axes—explaining observed HbA1c and weight reductions and CV risk benefits (https://doi.org/10.1007/s00592-024-02300-6) (ciardullo2024glp1gipreceptorcoagonists pages 1-2). - IR pathway targeting: lowering lipid intermediates (DAG/ceramides), relieving mTOR/S6K negative feedback, and reducing ER/mitochondrial stress align with mechanistic maps of IR (https://doi.org/10.1007/s43152-024-00056-3) (chandrasekaran2024cellularandmolecular pages 1-2). - Mitochondrial/oxidative stress interventions and improving mitophagy/MQC are rational to interrupt early cross-tissue drivers of disease (https://doi.org/10.3390/ijms25031504) (veluthakal2024mitochondrialdysfunctionoxidative pages 1-2).

Limitations Some lines of evidence (e.g., direct human islet amyloid dynamics and detailed UPR arm contributions) are summarized from integrative reviews that compiled 2023–2024 studies; where possible, we prioritized peer-reviewed 2024 primary/large-scale human evidence (madsen2024geneticarchitectureof pages 1-2, chandrasekaran2024cellularandmolecular pages 1-2, ciardullo2024glp1gipreceptorcoagonists pages 1-2). (młynarska2025type2diabetes pages 16-18, veluthakal2024mitochondrialdysfunctionoxidative pages 1-2).

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