Diabetes mellitus

Complex MONDO:0005015 Show in embeddings browser endocrine pancreas disorder glucose metabolism disease

Diabetes mellitus is a heterogeneous group of disorders characterized by chronic hyperglycemia due to defects in insulin secretion, insulin action, or both.

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Classifications

Harrison's Chapter

📘

Definitions

Metabolic hyperglycemia case definition for diabetes mellitus

Diabetes mellitus is defined as a heterogeneous metabolic disease group with chronic hyperglycemia as the core clinical feature.

CASE_DEFINITION Cross-subtype disease-level framing

Show evidence (1 reference)

PMID:26621825 SUPPORT Other

"Diabetes mellitus is a complex metabolic disorder associated with an increased risk of microvascular and macrovascular disease; its main clinical characteristic is hyperglycaemia."

Supports a broad disease-level case framing centered on chronic hyperglycemia and vascular risk.

USPSTF screening phenotype algorithm for prediabetes and type 2 diabetes

Screening-focused phenotype definition for identifying adults who should undergo testing for prediabetes and type 2 diabetes in primary care.

PHENOTYPE_ALGORITHM Asymptomatic nonpregnant adults in primary care settings

Inclusion criteria

Adults aged 35 to 70 years Age-based screening eligibility window used in the USPSTF recommendation.
Overweight or obesity Screening eligibility is defined for adults with overweight or obesity.

Show evidence (2 references)

PMID:34427594 SUPPORT Other

"The USPSTF recommends screening for prediabetes and type 2 diabetes in adults aged 35 to 70 years who have overweight or obesity."

Supports the population eligibility criteria in this screening algorithm definition.

PMID:34427594 SUPPORT Other

"Clinicians should offer or refer patients with prediabetes to effective preventive interventions."

Supports the intervention linkage embedded in this screening algorithm framework.

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Subtypes

mondo direct subclass

type 1 diabetes mellitus MONDO:0005147

Autoimmune-predominant diabetes with progressive beta-cell loss.

latent autoimmune diabetes in adults

Show evidence (2 references)

PMID:37960733 PARTIAL Human Clinical

"Most patients with immune checkpoint inhibitor-induced type 1 diabetes are reported to have no combination of autoimmune disease."

This case report adds recent clinical context for an immune-therapy-associated type 1 diabetes presentation.

PMID:34515603 PARTIAL Human Clinical

"It is fortuitous that a 14 year old boy with what was unequivocally type 1 diabetes was selected to be the first insulin recipient, and the rest is history."

This historical review provides context on early clinical recognition of unequivocal type 1 diabetes.

type 2 diabetes mellitus MONDO:0005148

Diabetes with dominant insulin resistance and relative insulin deficiency.

lipoatrophic diabetes

gestational diabetes MONDO:0005406

Pregnancy-associated diabetes characterized by glucose intolerance first emerging or first recognized during pregnancy, typically driven by pregnancy-induced insulin resistance with inadequate beta-cell compensation.

Show evidence (4 references)

PMID:33550962 SUPPORT Human Clinical

"Gestational diabetes mellitus (GDM) is one of the most common medical complications of pregnancy and is defined as glucose intolerance that first emerges or is first recognized during pregnancy."

This supports the diagnostic framing of GDM as pregnancy-onset glucose intolerance.

PMID:32679915 SUPPORT Human Clinical

"Gestational diabetes mellitus (GDM), characterized by a transitory form of diabetes induced by insulin resistance and pancreatic β-cell dysfunction during pregnancy, has been identified as one of the major obstacles in achieving improved maternal and child health."

This supports core pathophysiologic features of gestational diabetes, including insulin resistance and beta-cell dysfunction.

PMID:31345518 SUPPORT Human Clinical

"Although it has been accepted for decades that women with gestational diabetes mellitus (GDM) are at high risk for future development of type 2 diabetes, vigorous debate regarding the value of detecting and treating GDM has persisted into the twenty-first century."

This supports long-term progression risk after GDM, adding clinically relevant subtype context.

+ 1 more reference

monogenic diabetes MONDO:0015967

Inherited single-gene forms of diabetes spanning neonatal diabetes, MODY subtypes, and related syndromic/mitochondrial presentations.

neonatal diabetes mellitus
maturity-onset diabetes of the young

Show evidence (8 references)

PMID:33409956 SUPPORT Human Clinical

"OBJECTIVE: To describe the spectrum of neonatal diabetes mellitus (NDM), document new mutations, and review published Indian literature on the etiology of NDM."

This supports inherited monogenic heterogeneity within early-onset diabetes presentations.

PMID:33409956 SUPPORT Human Clinical

"CONCLUSIONS: The authors describe 5 novel mutations, in the EIF2AK3, ABCC8, and GCK genes, a homozygous mutation at the ABCC8 locus presenting as TNDM, an obscure phenotype of the GCK gene mutation, and hyperglycemia-induced hemichorea-hemiballismus in a patient with TRMA."

This supports diverse causative inherited variants in monogenic diabetes subtypes.

PMID:29931562 SUPPORT Human Clinical

"Monogenic diabetes is due to single gene defects that primarily affect beta cell function with more than 30 different genes reported."

This supports monogenic diabetes as a single-gene disease group with broad genetic heterogeneity.

+ 5 more references

maternally-inherited diabetes and deafness MONDO:0010785

Mitochondrial diabetes subtype associated with sensorineural hearing loss.

type 5 diabetes mellitus MONDO:1010179

Recently formalized diabetes class (often framed as malnutrition-related diabetes) with insulin-deficient phenotypes in undernourished populations, while some contemporary literature frames overlapping cases as pancreatogenic fibro-inflammatory disease; classification boundaries remain under active debate.

Show evidence (4 references)

PMID:40657327 SUPPORT Human Clinical

"Type 5 diabetes mellitus (T5DM), recently redefined as malnutrition-related diabetes, represents a distinct form of severe insulin-deficient diabetes that arises from chronic undernutrition, particularly during childhood and adolescence."

This supports malnutrition-related etiology and insulin-deficient biology in type 5 diabetes.

PMID:41180768 SUPPORT Human Clinical

"It disproportionately affects malnourished teens in low- and middle-income countries and is therefore, also known as malnutrition-related diabetes."

This supports demographic and epidemiologic context for type 5 diabetes burden.

PMID:41456634 SUPPORT Human Clinical

"Emerging evidence has revealed a unique pathophysiology, prompting the International Diabetes Federation (IDF) to officially recognize MRDM as "Type 5 Diabetes" (T5D)."

This supports formal recognition and mechanistic distinction of type 5 diabetes in contemporary classification discourse.

+ 1 more reference

diabetic ketoacidosis MONDO:0012819

Included here because MONDO currently places it as a direct subclass of diabetes mellitus, although it is often treated clinically as an acute complication state.

Show evidence (1 reference)

PMID:37960733 PARTIAL Human Clinical

"Regrettably, the patient's personal decision to discontinue medication for a single day led to the emergence of acute ketoacidosis, coupled with a recurrence of psoriasis vulgaris."

This case report provides real-world context for abrupt diabetic ketoacidosis decompensation.

mondo nested subclass

latent autoimmune diabetes in adults MONDO:0850306

Adult-onset autoimmune diabetes within the type 1 spectrum.

lipoatrophic diabetes MONDO:0005827

Diabetes associated with lipodystrophy/lipoatrophy phenotypes.

neonatal diabetes mellitus MONDO:0016391

Monogenic diabetes presenting in early infancy.

transient neonatal diabetes mellitus
permanent neonatal diabetes mellitus

Show evidence (3 references)

PMID:33409956 SUPPORT Human Clinical

"OBJECTIVE: To describe the spectrum of neonatal diabetes mellitus (NDM), document new mutations, and review published Indian literature on the etiology of NDM."

This case-series report supports the heterogeneous clinical and genetic spectrum of neonatal diabetes mellitus.

PMID:39344692 SUPPORT Human Clinical

"Neonatal diabetes mellitus (NDM), defined as diabetes with an onset during the first 6 months of life, is a rare form of monogenic diabetes."

This supports neonatal diabetes as a rare monogenic subtype with infancy-onset diagnostic framing.

PMID:39344692 SUPPORT Human Clinical

"As a consequence, the list of NDM genes now exceeds 40."

This supports extensive genetic heterogeneity within neonatal monogenic diabetes.

transient neonatal diabetes mellitus MONDO:0020525

Neonatal diabetes with early remission and possible relapse later in life.

Show evidence (1 reference)

PMID:33409956 SUPPORT Human Clinical

"Four patients had transient diabetes"

This supports transient neonatal diabetes as a clinically observed subtype with identifiable monogenic etiologies.

permanent neonatal diabetes mellitus MONDO:0100164

Persistent neonatal-onset diabetes requiring ongoing management.

Show evidence (1 reference)

PMID:33409956 SUPPORT Human Clinical

"7 had permanent diabetes"

This supports permanent neonatal diabetes as a clinically observed subtype with diverse genetic causes.

maturity-onset diabetes of the young MONDO:0018911

Monogenic diabetes group classically characterized by early-onset non-ketotic diabetes.

maturity-onset diabetes of the young type 1
maturity-onset diabetes of the young type 2
maturity-onset diabetes of the young type 3
maturity-onset diabetes of the young type 4
maturity-onset diabetes of the young type 6
maturity-onset diabetes of the young type 7
maturity-onset diabetes of the young type 8
maturity-onset diabetes of the young type 9
maturity-onset diabetes of the young type 10
maturity-onset diabetes of the young type 11
maturity-onset diabetes of the young, type 12
maturity-onset diabetes of the young type 13
maturity-onset diabetes of the young type 14
renal cysts and diabetes syndrome

Show evidence (1 reference)

PMID:29931562 SUPPORT Human Clinical

"Now, MODY represents a clinically heterogeneous group of autosomal-dominant disorders caused by mutations in genes involved in beta cell development and insulin secretion and is the most common form of monogenic diabetes, estimated to account for 1–2% of diabetes cases (see Table 1) [13]."

This supports MODY as a heterogeneous autosomal-dominant monogenic diabetes group.

maturity-onset diabetes of the young type 1 MONDO:0007452

MONDO-defined MODY subtype.

maturity-onset diabetes of the young type 2 MONDO:0007453

MONDO-defined MODY subtype.

maturity-onset diabetes of the young type 3 MONDO:0010894

MONDO-defined MODY subtype.

maturity-onset diabetes of the young type 4 MONDO:0011667

MONDO-defined MODY subtype.

maturity-onset diabetes of the young type 6 MONDO:0011668

MONDO-defined MODY subtype.

maturity-onset diabetes of the young type 7 MONDO:0012513

MONDO-defined MODY subtype.

maturity-onset diabetes of the young type 8 MONDO:0012348

MONDO-defined MODY subtype.

maturity-onset diabetes of the young type 9 MONDO:0012818

MONDO-defined MODY subtype.

maturity-onset diabetes of the young type 10 MONDO:0013240

MONDO-defined MODY subtype.

maturity-onset diabetes of the young type 11 MONDO:0013242

MONDO-defined MODY subtype.

maturity-onset diabetes of the young, type 12 MONDO:0978299

MONDO-defined MODY subtype.

maturity-onset diabetes of the young type 13 MONDO:0014589

MONDO-defined MODY subtype.

maturity-onset diabetes of the young type 14 MONDO:0014674

MONDO-defined MODY subtype.

renal cysts and diabetes syndrome MONDO:0007669

Syndromic form overlapping MODY-related monogenic diabetes spectrum.

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Pathophysiology

Chronic Hyperglycemia (shared diabetic complication cascade)

Sustained hyperglycemia is the shared, subtype-independent driver of diabetic tissue injury across every form of diabetes mellitus. Whatever the upstream route (autoimmune beta-cell destruction in type 1, insulin resistance with beta-cell secretory failure in type 2, undernutrition-associated beta-cell impairment in type 5, or a monogenic beta-cell defect), chronic hyperglycemia feeds one conserved cascade: hyperglycemia-induced oxidative and AGE-RAGE stress, endothelial dysfunction and vascular inflammation, micro- and macrovascular injury, and diabetic end-organ complications (kidney disease, retinopathy, neuropathy, atherosclerotic cardiovascular disease). That cascade is captured once in the diabetic_vascular_complications module, to which this node conforms; the per-type entries elaborate their own routes to hyperglycemia.

glucose homeostasis GO:0042593 ⚠ ABNORMAL

Show evidence (1 reference)

PMID:32872570 SUPPORT Human Clinical

"According to the World Health Organization (WHO) diabetes mellitus is a chronic, metabolic disease characterized by elevated levels of blood glucose, which leads over time to damage to the heart, vasculature, eyes, kidneys and nerves."

Supports chronic hyperglycemia as the shared driver of multiorgan diabetic complications, the mechanism captured by the diabetic_vascular_complications module this node conforms to.

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Phenotypes

Cardiovascular 3

Coronary artery disease OCCASIONAL Coronary artery atherosclerosis HP:0001677

Show evidence (1 reference)

PMID:39742220 SUPPORT Human Clinical

"The predominant form of CVD among diabetic patients is coronary artery disease (CAD), accounting for 29.4% of cases."

This directly supports coronary artery disease as a dominant macrovascular phenotype in diabetes.

Cerebrovascular disease OCCASIONAL Stroke HP:0001297

Show evidence (1 reference)

PMID:32872570 SUPPORT Human Clinical

"T2DM leads to a two- to four-fold increase in the mortality rate of adults from heart disease and stroke and is associated with both micro- and macro-vascular complications, the latter consisting of accelerated atherosclerosis leading to severe peripheral vascular disease, premature coronary..."

This supports increased cerebrovascular disease risk as a macrovascular phenotype in diabetes.

Peripheral artery disease OCCASIONAL Peripheral arterial stenosis HP:0004950

Show evidence (1 reference)

PMID:32872570 SUPPORT Human Clinical

This supports peripheral artery disease as a major macrovascular complication phenotype in diabetes.

Endocrine 2

Hyperinsulinemia FREQUENT Hyperinsulinemia HP:0000842

Show evidence (1 reference)

PMID:29939616 SUPPORT Human Clinical

"Insulin resistance impairs glucose disposal, resulting in a compensatory increase in beta-cell insulin production and hyperinsulinemia."

This supports hyperinsulinemia as a compensatory metabolic phenotype in insulin-resistant diabetes pathways.

Diabetic ketoacidosis OCCASIONAL Diabetic ketoacidosis HP:0001953

Show evidence (2 references)

PMID:39556629 SUPPORT Human Clinical

"Diabetic ketoacidosis (DKA) is a life-threatening complication of type 1 and type 2 diabetes resulting from an absolute or relative insulin deficiency."

This directly supports diabetic ketoacidosis as an established acute phenotype across diabetes subtypes.

PMID:37960733 PARTIAL Human Clinical

"Regrettably, the patient's personal decision to discontinue medication for a single day led to the emergence of acute ketoacidosis, coupled with a recurrence of psoriasis vulgaris."

This case report provides recent clinical context for acute ketoacidosis recurrence in an immune-therapy-associated diabetes presentation.

Eye 1

Diabetic retinopathy OCCASIONAL Retinopathy HP:0000488

Show evidence (1 reference)

PMID:39158206 SUPPORT Human Clinical

"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."

This supports diabetic retinopathy as a major microvascular phenotype contributing to vision-threatening outcomes.

Genitourinary 4

Albuminuria OCCASIONAL Albuminuria HP:0012592

Show evidence (1 reference)

PMID:41632731 SUPPORT Other

"Changes in glomerular hemodynamics, glomerular and tubular hypertrophy, hyperfiltration, overactivation of the renin-angiotensin-aldosterone system (RAAS), podocyte injury, inflammation, oxidative stress, renal hypoxia, mitochondrial injury, and epigenetic changes are the main mechanisms leading..."

This directly supports albuminuria as a major renal phenotype in diabetic kidney disease pathways.

Chronic kidney disease OCCASIONAL Chronic kidney disease HP:0012622

Show evidence (2 references)

PMID:41632731 SUPPORT Other

"Diabetes mellitus (DM) is the most prevalent cause of CKD and end-stage kidney disease (ESKD), requiring renal replacement therapy (RRT)."

This supports chronic kidney disease as a common and clinically important diabetes-associated phenotype.

PMID:36619566 PARTIAL Human Clinical

"Renal biopsy might aid in the definitive diagnosis of DKD, NDKD, and NDKD combined with DKD."

This case report adds context that CKD in diabetes may include coexisting non-diabetic kidney disease.

Polyuria FREQUENT Polyuria HP:0000103

Show evidence (1 reference)

PMID:39556629 SUPPORT Human Clinical

"Polyuria and polydipsia are the most common symptoms, followed by nausea, vomiting, abdominal pain, weight loss, severe fatigue, dyspnea, and preceding febrile illness."

This supports polyuria as a common symptomatic phenotype in diabetic metabolic decompensation.

Glycosuria FREQUENT Glycosuria HP:0003076

Show evidence (1 reference)

PMID:26621825 PARTIAL Other

"The central role of insulin in glucose metabolism regulation was clearly demonstrated during the early 1920s, when Banting, Best, Collip and Macleod successfully reduced blood glucose levels and glycosuria in a patient treated with a substance purified from bovine pancreata."

This supports glycosuria as a clinically recognized glucose-related phenotype in diabetes history and pathophysiology.

Metabolism 2

Hyperglycemia VERY_FREQUENT Hyperglycemia HP:0003074

Show evidence (1 reference)

PMID:39556629 SUPPORT Human Clinical

"Traditionally, DKA has been diagnosed by the triad of hyperglycemia (blood glucose greater than 250 mg/dL), metabolic acidosis (pH less than 7.3, serum bicarbonate less than 18 mEq/L, anion gap greater than 10 mEq/L), and elevated serum (preferred) or urine ketones."

This supports hyperglycemia as a core defining metabolic phenotype in decompensated diabetes presentations.

Impaired glucose tolerance VERY_FREQUENT Glucose intolerance HP:0001952

Show evidence (1 reference)

PMID:34122344 SUPPORT Human Clinical

"On top of these staggering figures, are the number of people with impaired glucose tolerance (IGT) or metabolic syndrome with 373.9 million in 2019 (7.5%) and predicted rise to 548.4 million (8.6%) by 2045 (2)."

This supports impaired glucose tolerance as a highly prevalent dysglycemic phenotype relevant to diabetes progression.

Nervous System 3

Diabetic neuropathy OCCASIONAL Peripheral neuropathy HP:0009830

Show evidence (1 reference)

PMID:39158206 SUPPORT Human Clinical

This supports diabetic neuropathy as a major microvascular phenotype in diabetes.

Polydipsia FREQUENT Polydipsia HP:0001959

Show evidence (1 reference)

PMID:39556629 SUPPORT Human Clinical

"Polyuria and polydipsia are the most common symptoms, followed by nausea, vomiting, abdominal pain, weight loss, severe fatigue, dyspnea, and preceding febrile illness."

This supports polydipsia as a common symptomatic phenotype in diabetic metabolic decompensation.

Polyphagia OCCASIONAL Polyphagia HP:0002591

Show evidence (1 reference)

PMID:35309606 SUPPORT Human Clinical

"Among clinical features at presentation, 56.5% presented with polyuria, 34.8% with polydipsia, 21.7% with polyphagia, 39.1% with weight loss."

This supports polyphagia as a presenting phenotype in a subset of pediatric type 1 diabetes cases.

Constitutional 1

Fatigue FREQUENT Fatigue HP:0012378

Show evidence (1 reference)

PMID:39556629 SUPPORT Human Clinical

"Polyuria and polydipsia are the most common symptoms, followed by nausea, vomiting, abdominal pain, weight loss, severe fatigue, dyspnea, and preceding febrile illness."

This supports fatigue as a common symptomatic phenotype during diabetic ketoacidosis presentations.

Growth 2

Weight loss FREQUENT Weight loss HP:0001824

Show evidence (1 reference)

PMID:35309606 SUPPORT Human Clinical

"Among clinical features at presentation, 56.5% presented with polyuria, 34.8% with polydipsia, 21.7% with polyphagia, 39.1% with weight loss."

This supports weight loss as a common presenting phenotype in pediatric type 1 diabetes.

Obesity FREQUENT Obesity HP:0001513

Show evidence (1 reference)

PMID:34427594 PARTIAL Other

"The USPSTF recommends screening for prediabetes and type 2 diabetes in adults aged 35 to 70 years who have overweight or obesity."

This supports obesity as a key and common risk-associated phenotype in type 2 diabetes pathways.

🧬

Genetic Associations

HLA-DQA1 (Susceptibility)

Gene: HLA-DQA1 hgnc:4942

Show evidence (1 reference)

PMID:22184118 SUPPORT Human Clinical

"HLA-DQ2 and HLA-DQ8 are strongly predisposing haplotypes for type 1 diabetes (T1D)."

This supports HLA-DQA1-containing DQ haplotypes as major susceptibility contributors in type 1 diabetes.

HLA-DQB1 (Susceptibility)

Gene: HLA-DQB1 hgnc:4944

Show evidence (1 reference)

PMID:22184118 SUPPORT Human Clinical

"HLA-DQ2 and HLA-DQ8 are strongly predisposing haplotypes for type 1 diabetes (T1D)."

This supports HLA-DQB1-containing DQ haplotypes as major susceptibility contributors in type 1 diabetes.

INS (Susceptibility)

Gene: INS hgnc:6081

Show evidence (1 reference)

PMID:26404926 SUPPORT Human Clinical

"The highest non-HLA genetic contribution arises from the INS, PTPN22, CTLA4, and IL2RA genes, with the latter three genes also contributing to susceptibility to other autoimmune diseases (17)."

This supports INS as a major non-HLA susceptibility gene in type 1 diabetes.

PTPN22 (Susceptibility)

Gene: PTPN22 hgnc:9652

Show evidence (1 reference)

PMID:26404926 SUPPORT Human Clinical

"The highest non-HLA genetic contribution arises from the INS, PTPN22, CTLA4, and IL2RA genes, with the latter three genes also contributing to susceptibility to other autoimmune diseases (17)."

This supports PTPN22 as a major non-HLA susceptibility gene in type 1 diabetes.

IL2RA (Susceptibility)

Gene: IL2RA hgnc:6008

Show evidence (1 reference)

PMID:26404926 SUPPORT Human Clinical

"The highest non-HLA genetic contribution arises from the INS, PTPN22, CTLA4, and IL2RA genes, with the latter three genes also contributing to susceptibility to other autoimmune diseases (17)."

This supports IL2RA as a major non-HLA susceptibility gene in type 1 diabetes.

CTLA4 (Susceptibility)

Gene: CTLA4 hgnc:2505

Show evidence (1 reference)

PMID:26404926 SUPPORT Human Clinical

"The highest non-HLA genetic contribution arises from the INS, PTPN22, CTLA4, and IL2RA genes, with the latter three genes also contributing to susceptibility to other autoimmune diseases (17)."

This supports CTLA4 as a major non-HLA susceptibility gene in type 1 diabetes.

TCF7L2 (Risk factor)

Gene: TCF7L2 hgnc:11641

Show evidence (1 reference)

PMID:17463248 SUPPORT Human Clinical

"CDKN2A and CDKN2B, and confirm that variants near TCF7L2, SLC30A8, HHEX, FTO, PPARG, and KCNJ11 are associated with T2D risk."

This supports TCF7L2 as an established type 2 diabetes risk locus.

PPARG (Risk factor)

Gene: PPARG hgnc:9236

Show evidence (1 reference)

PMID:17463248 SUPPORT Human Clinical

"CDKN2A and CDKN2B, and confirm that variants near TCF7L2, SLC30A8, HHEX, FTO, PPARG, and KCNJ11 are associated with T2D risk."

This supports PPARG as an established type 2 diabetes risk locus.

KCNJ11 (Risk factor)

Gene: KCNJ11 hgnc:6257

Show evidence (2 references)

PMID:17463248 SUPPORT Human Clinical

"CDKN2A and CDKN2B, and confirm that variants near TCF7L2, SLC30A8, HHEX, FTO, PPARG, and KCNJ11 are associated with T2D risk."

This supports KCNJ11 as an established type 2 diabetes risk locus.

PMID:33046911 PARTIAL Human Clinical

"We show that, among 74,629 individuals, this burden is probably driven by the pathogenic variants found in GCK, and to a lesser extent in HNF4A, KCNJ11, HNF1B and ABCC8."

This supports additional pathogenic-variant burden involving KCNJ11 in people labeled as common type 2 diabetes.

SLC30A8 (Risk factor)

Gene: SLC30A8 hgnc:20303

Show evidence (1 reference)

PMID:17463248 SUPPORT Human Clinical

"CDKN2A and CDKN2B, and confirm that variants near TCF7L2, SLC30A8, HHEX, FTO, PPARG, and KCNJ11 are associated with T2D risk."

This supports SLC30A8 as an established type 2 diabetes risk locus.

HNF1A (Causative)

Gene: HNF1A hgnc:11621

Show evidence (3 references)

PMID:29931562 SUPPORT Human Clinical

"Although pathogenic mutations in any of the 14 genes may result in MODY, mutations in GCK, HNF1A, and HNF4A are the most common causes of MODY, representing 52, 10, and 32% of MODY cases in the UK, respectively [15, 33]."

This supports HNF1A as a common causative monogenic diabetes gene within MODY.

PMID:35299962 SUPPORT Human Clinical

"Some single nucleotide polymorphisms (SNPs) cause maturity-onset diabetes of the young type 3 (MODY3) while some others do not cause MODY3 but increase the susceptibility to T2D or GDM."

This supports HNF1A variant-dependent effects across monogenic MODY and broader diabetes susceptibility.

PMID:36178555 SUPPORT Human Clinical

"When we considered probands of Group 1 with a parental history of hyperglycemia, 58.3% (21/36) had a positive genetic test for GCK or HNF1A genes"

This supports frequent involvement of HNF1A in clinically selected pediatric monogenic diabetes evaluations.

HNF4A (Causative)

Gene: HNF4A hgnc:5024

Show evidence (2 references)

PMID:29931562 SUPPORT Human Clinical

This supports HNF4A as a common causative gene in monogenic MODY.

PMID:33046911 PARTIAL Human Clinical

"We show that, among 74,629 individuals, this burden is probably driven by the pathogenic variants found in GCK, and to a lesser extent in HNF4A, KCNJ11, HNF1B and ABCC8."

This supports measurable HNF4A pathogenic-variant burden among people with common type 2 diabetes labels.

HNF1B (Causative)

Gene: HNF1B hgnc:11630

Show evidence (2 references)

PMID:29931562 SUPPORT Human Clinical

"HNF1B mutations account for less than 10% of all MODY cases [62, 63]."

This supports HNF1B as an established but less frequent monogenic MODY-related cause.

PMID:33046911 PARTIAL Human Clinical

"We show that, among 74,629 individuals, this burden is probably driven by the pathogenic variants found in GCK, and to a lesser extent in HNF4A, KCNJ11, HNF1B and ABCC8."

This supports clinically relevant HNF1B pathogenic-variant burden within type 2 diabetes cohorts.

ABCC8 (Causative)

Gene: ABCC8 hgnc:59

Show evidence (3 references)

PMID:33409956 SUPPORT Human Clinical

"2 with ABCC8 mutation"

This supports ABCC8 as a causal gene in monogenic neonatal diabetes cases.

PMID:33409956 SUPPORT Human Clinical

"The authors describe 5 novel mutations, in the EIF2AK3, ABCC8, and GCK genes"

This supports ABCC8 as a recurrently implicated monogenic diabetes gene.

PMID:33046911 PARTIAL Human Clinical

"We show that, among 74,629 individuals, this burden is probably driven by the pathogenic variants found in GCK, and to a lesser extent in HNF4A, KCNJ11, HNF1B and ABCC8."

This supports ABCC8 pathogenic-variant burden in subsets of clinically common type 2 diabetes populations.

GCK (Causative)

Gene: GCK hgnc:4195

Show evidence (4 references)

PMID:33409956 SUPPORT Human Clinical

"2 siblings with complete glucokinase deficiency"

This supports causal involvement of severe GCK defects in permanent neonatal diabetes.

PMID:33409956 SUPPORT Human Clinical

"The authors describe 5 novel mutations, in the EIF2AK3, ABCC8, and GCK genes"

This supports GCK as a monogenic diabetes gene represented in clinically curated cases.

PMID:33046911 SUPPORT Human Clinical

"We show that, among 74,629 individuals, this burden is probably driven by the pathogenic variants found in GCK, and to a lesser extent in HNF4A, KCNJ11, HNF1B and ABCC8."

This supports GCK as a major contributor to pathogenic monogenic-variant burden detected in type 2 diabetes cohorts.

+ 1 more reference

EIF2AK3 (Causative)

Gene: EIF2AK3 hgnc:3255

Show evidence (1 reference)

PMID:33409956 SUPPORT Human Clinical

"The authors describe 5 novel mutations, in the EIF2AK3, ABCC8, and GCK genes"

This supports EIF2AK3 as a causative gene in monogenic diabetes-related case series data.

WFS1 (Causative)

Gene: WFS1 hgnc:12762

Show evidence (1 reference)

PMID:28432734 SUPPORT Human Clinical

"Pathogenic variants in WFS1 (MIM# 606201) cause Wolfram syndrome (WS) type 1, a rare neurodegenerative disease characterized by DM and optic atrophy (OA)."

This supports WFS1 as a causative monogenic diabetes-syndrome gene.

ALMS1 (Causative)

Gene: ALMS1 hgnc:428

Show evidence (1 reference)

PMID:28432734 SUPPORT Human Clinical

"Pathogenic variants in the gene ALMS1 (MIM# 606844) on chromosomes 2p13.1 have been identified in patients with Alström syndrome (AS), an autosomal‐recessive disease characterized by retinal dystrophy, childhood obesity, type 2 DM, and sensorineural hearing loss"

This supports ALMS1 as a causative gene in syndromic monogenic diabetes.

CISD2 (Causative)

Gene: CISD2 hgnc:24212

Show evidence (1 reference)

PMID:28432734 SUPPORT Human Clinical

"Pathogenic variants in the CISD2 gene (MIM# 611507) have been identified in patients with WS type 2"

This supports CISD2 as a causative gene in a monogenic diabetes syndrome.

SLC19A2 (Causative)

Gene: SLC19A2 hgnc:10938

Show evidence (1 reference)

PMID:28432734 SUPPORT Human Clinical

"The gene responsible, SLC19A2 (MIM# 603941), is located on chromosome 1q24.2, consists of six exons with 497 amino acids spanning a 22.5‐kb genomic region."

This supports SLC19A2 as a causative gene in a syndromic monogenic diabetes form.

💊

Medical Actions

Insulin therapy

Action: insulin treatment Ontology label: Injected Insulin Diabetes Therapy NCIT:C179441

Agent: insulin CHEBI:145810

Essential replacement therapy for insulin-deficient diabetes, and adjunctive therapy in advanced type 2 diabetes.

Show evidence (1 reference)

PMID:33970586 SUPPORT Human Clinical

"The mainstay of management is a regimen of multiple daily injections of insulin or continuous subcutaneous insulin delivered via an insulin pump."

This supports insulin replacement as core treatment in insulin-deficient diabetes pathways.

Metformin

Action: targeted therapy Ontology label: Targeted Therapy NCIT:C93352

Agent: metformin CHEBI:6801

First-line oral pharmacotherapy in type 2 diabetes to reduce hepatic glucose production and improve insulin sensitivity.

Show evidence (1 reference)

PMID:30150719 SUPPORT Human Clinical

"The biguanide drug metformin (N,N-dimethylbiguanide) works principally through inhibition of HGP, although enhanced glucose disposal has also been reported in some studies1."

This supports metformin as a glucose-lowering therapy acting largely via suppression of hepatic glucose production.

Dietary intervention

Action: dietary intervention Ontology label: Dietary Intervention NCIT:C15447

Structured nutrition-focused intervention to improve glycemic control, weight trajectory, and cardiometabolic risk.

Show evidence (2 references)

PMID:38243953 SUPPORT Human Clinical

"The cornerstone of T2DM prevention is the control of modifiable risk factors and lifestyle changes to potentially prevent diabetes."

This supports dietary intervention as a core modifiable lifestyle strategy in diabetes prevention and care.

PMID:34427594 PARTIAL Other

"Clinicians should offer or refer patients with prediabetes to effective preventive interventions."

This supports evidence-based preventive lifestyle intervention programs relevant to dietary management.

Physical activity increase

Action: aerobic exercise therapy Ontology label: Physical Therapy NCIT:C15302

Increased habitual and structured physical activity to improve insulin sensitivity and glycemic outcomes.

Show evidence (2 references)

PMID:38243953 SUPPORT Human Clinical

"The cornerstone of T2DM prevention is the control of modifiable risk factors and lifestyle changes to potentially prevent diabetes."

This supports physical-activity-focused lifestyle intervention as a core modifiable strategy in diabetes prevention and care.

PMID:34427594 PARTIAL Other

"Clinicians should offer or refer patients with prediabetes to effective preventive interventions."

This supports referral to effective preventive intervention programs that include physical activity components.

GLP-1 receptor agonists

Action: targeted therapy Ontology label: Targeted Therapy NCIT:C93352

Agent: semaglutide CHEBI:167574 liraglutide CHEBI:71193

Incretin-based therapies that improve glycemic control and support weight reduction.

Show evidence (2 references)

PMID:33068776 SUPPORT Human Clinical

"GLP-1RAs are recommended as the preferred first injectable glucose-lowering therapy for type 2 diabetes, even before insulin treatment."

This supports GLP-1 receptor agonists as a recommended major therapy class in type 2 diabetes management.

PMID:38639546 SUPPORT Other

"Use a GLP-1 agonist to reduce the risk for all-cause mortality, major adverse cardiovascular events, and stroke."

The 2024 ACP clinical guideline on newer type 2 diabetes treatments recommends GLP-1 receptor agonists to reduce all-cause mortality, major adverse cardiovascular events, and stroke.

SGLT2 inhibitors

Action: Pharmacotherapy NCIT:C15986

Agent: empagliflozin CHEBI:82720 dapagliflozin CHEBI:85078 canagliflozin CHEBI:73274

Oral agents that reduce plasma glucose by increasing urinary glucose excretion.

Show evidence (1 reference)

PMID:33441402 SUPPORT Human Clinical

"In patients with type 2 diabetes, SGLT-2 inhibitors and GLP-1 receptor agonists reduced cardiovascular and renal outcomes, with some differences in benefits and harms."

This supports SGLT2 inhibitors as an evidence-based therapy class with demonstrated outcome benefits in type 2 diabetes.

GLP-1/GIP dual agonists

Action: targeted therapy Ontology label: Targeted Therapy NCIT:C93352

Agent: tirzepatide CHEBI:194186

Dual incretin receptor agonists (e.g., tirzepatide) for enhanced glycemic and weight outcomes.

Show evidence (1 reference)

PMID:38831203 SUPPORT Human Clinical

"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 supports dual incretin agonism as a treatment strategy in type 2 diabetes pathways.

Teplizumab immunotherapy

Action: immunotherapy Ontology label: Immunotherapy NCIT:C15262

Agent: monoclonal antibody NCIT:C20401

CD3-targeted immunotherapy to delay progression from stage 2 to stage 3 type 1 diabetes.

Show evidence (1 reference)

PMID:36877454 SUPPORT Human Clinical

"In November 2022, teplizumab was approved in the USA to delay the onset of Stage 3 T1D in adults and pediatric patients 8 years of age and older with Stage 2 T1D"

This supports teplizumab as subtype-specific disease-modifying therapy in early type 1 diabetes.

Blood glucose monitoring

Self-monitoring of blood glucose and ketones when indicated to guide day-to-day therapeutic adjustments.

Show evidence (1 reference)

PMID:32256447 SUPPORT Human Clinical

"Regular self-monitoring of blood glucose levels, and ketones when indicated, is an essential component of type 1 diabetes (T1D) management."

This supports frequent glucose monitoring as a core management component.

Continuous glucose monitoring

Real-time sensor-based glycemia tracking that improves time-in-range and HbA1c outcomes.

Show evidence (1 reference)

PMID:34872983 SUPPORT Human Clinical

"After CGM introduction, the odds ratio (OR) of achieving the HbA1c target of <7.0% improved at 12 months (OR 2.5, P < 0.001) and was maintained at 24 months"

This supports CGM use for improved long-term glycemic target attainment.

🌍

Environmental Factors

Environmental determinants of diabetes susceptibility and progression

Environmental contexts modify susceptibility and progression across diabetes subtypes rather than a single uniform etiologic pathway.

Show evidence (1 reference)

PMID:27980006 SUPPORT Human Clinical

"International experts in genetics, immunology, metabolism, endocrinology, and systems biology discussed genetic and environmental determinants of type 1 and type 2 diabetes risk and progression, as well as complications."

This supports broad environmental contributions to diabetes susceptibility and progression.

Exogenous triggers of islet autoimmunity in type 1 diabetes pathways

In genetically predisposed individuals, exogenous exposures can initiate autoimmune processes targeting pancreatic beta cells.

Show evidence (1 reference)

PMID:10522815 SUPPORT Human Clinical

"Various exogenous triggers, such as certain dietary factors and viruses, are thought to induce the autoimmune process leading in some individuals to extensive beta-cell destruction and ultimately to the clinical manifestation of type 1 diabetes."

This supports environmental exposure-triggered autoimmunity as a key risk relationship in type 1 diabetes biology.

Diabetogenic pharmacotherapy exposure

Exposure to medications with diabetogenic effects can precipitate persistent or transient diabetes phenotypes.

Show evidence (2 references)

PMID:36106423 SUPPORT Human Clinical

"Drug-induced diabetes mellitus (DIDM) could be defined as a heterogenic group of diabetes caused by pharmacotherapy."

This case-based review supports medication exposure as an environmental contributor to diabetes onset.

PMID:36106423 PARTIAL Human Clinical

"The DIDM is considered to be reversible after discontinuation of diabetogenic treatment, but there is a risk of persistence, which is related to the duration of treatment, prescribed medication, and body mass index."

This adds context on heterogeneity and persistence risk in pharmacotherapy-associated diabetes.

Immune checkpoint inhibitor exposure

Immune checkpoint inhibitor therapy can trigger insulin-deficient diabetes with abrupt decompensation in some patients.

Show evidence (1 reference)

PMID:37960733 SUPPORT Human Clinical

"The clinical manifestations of immune checkpoint inhibitor induced diabetes mellitus are variable, and in this case the patient presented with unique primary symptoms."

This case report supports checkpoint inhibitor exposure as a distinct treatment-associated diabetes trigger context.

Viral infection exposure

Enteroviral and other viral exposures are implicated as triggers of islet autoimmunity in susceptible individuals.

Show evidence (1 reference)

PMID:10522815 SUPPORT Human Clinical

This supports viral exposure as an environmental trigger linked to autoimmune diabetes onset.

Early dietary exposures

Early-life dietary exposures (e.g., infant feeding patterns and specific food antigens) are investigated as autoimmune-modulating factors.

Show evidence (1 reference)

PMID:10522815 SUPPORT Human Clinical

This supports early dietary exposures as environmental modulators of autoimmune risk in susceptible individuals.

Sedentary lifestyle

Low physical activity patterns contribute to insulin resistance and cardiometabolic risk amplification.

Show evidence (1 reference)

PMID:41632731 SUPPORT Other

"Type II DM remains the most prevalent subtype, as it is closely related to metabolic syndrome, whose prevalence is also rising with a sedentary lifestyle and Western diet."

This supports sedentary lifestyle as an environmental contributor linked to type 2 diabetes epidemiology and progression.

High-calorie diet

Energy-dense dietary patterns promote positive energy balance, adiposity, and insulin resistance.

Show evidence (1 reference)

PMID:29939616 SUPPORT Human Clinical

"This concept may be clinically valuable, suggesting that hyperinsulinemia associated with excess caloric intake may drive the metabolic dysfunction associated with insulin resistance."

This supports excess caloric intake as an environmental dietary driver of insulin-resistant diabetes pathways.

Obesogenic environment

Environmental contexts favoring obesity (dietary excess, low activity, and social determinants) amplify diabetes susceptibility.

Show evidence (1 reference)

PMID:34427594 PARTIAL Other

"The USPSTF recommends screening for prediabetes and type 2 diabetes in adults aged 35 to 70 years who have overweight or obesity."

This supports obesity-promoting environmental context as a major risk-linked feature in type 2 diabetes prevention and care.

🔬

Biochemical Markers

Blood glucose (Elevated)

Show evidence (1 reference)

PMID:6378696 PARTIAL Human Clinical

"Elevations of fasting blood glucose and peak glucose during oral glucose tolerance tests were not observed until the year before onset of clinically overt diabetes."

This supports elevated blood glucose as a defining biochemical abnormality in progression to overt diabetes.

Hemoglobin A1c (HbA1c) (Elevated)

Show evidence (1 reference)

PMID:22847316 SUPPORT Human Clinical

"The measure of glycated hemoglobin (HbA1c) concentration is the gold standard of glycemic control index in diabetes management and is well known as a marker for diabetes complications."

This supports elevated HbA1c as a central biochemical marker in diabetes management and complication risk.

🔀

Differential Diagnoses

Conditions with similar clinical presentations that must be differentiated from Diabetes mellitus:

diabetes insipidus Not Yet Curated MONDO:0004782

Overlapping Features Polyuria-polydipsia syndrome that can mimic symptomatic presentations of diabetes mellitus.

Distinguishing Features

Diabetes insipidus produces large volumes of dilute urine from AVP-axis dysfunction rather than persistent hyperglycemia-driven osmotic diuresis.

Show evidence (2 references)

PMID:27156759 SUPPORT Human Clinical

"Diabetes insipidus (DI) is a syndrome characterized by the excretion of abnormally large volumes of dilute urine."

This supports the key overlapping presentation of marked polyuria that often enters the differential of diabetes symptoms.

PMID:39556629 PARTIAL Human Clinical

"Polyuria and polydipsia are the most common symptoms, followed by nausea, vomiting, abdominal pain, weight loss, severe fatigue, dyspnea, and preceding febrile illness."

This supports symptom overlap (polyuria/polydipsia) with diabetes mellitus presentations.

primary polydipsia Not Yet Curated MONDO:0040870

Overlapping Features Excessive fluid intake syndrome in which polydipsia and polyuria can be confused with diabetes-related symptoms.

Distinguishing Features

Primary polydipsia is driven by excessive fluid intake with AVP suppression and lacks persistent diabetes pathophysiology.

Show evidence (2 references)

PMID:27156759 SUPPORT Human Clinical

"primary polydipsia, due to suppression of AVP secretion by excessive fluid intake;"

This supports primary polydipsia as a distinct non-diabetes cause of polyuria-polydipsia.

PMID:39556629 PARTIAL Human Clinical

"Polyuria and polydipsia are the most common symptoms, followed by nausea, vomiting, abdominal pain, weight loss, severe fatigue, dyspnea, and preceding febrile illness."

This supports symptom overlap that can create diagnostic confusion with diabetes mellitus.

Cushing syndrome MONDO:0018912

Overlapping Features Hypercortisolism can present with hyperglycemia and metabolic findings that overlap with diabetes mellitus.

Distinguishing Features

Cushingoid physical findings and biochemical cortisol-axis testing help distinguish this endocrine disorder from primary diabetes categories.

Show evidence (1 reference)

PMID:37432427 SUPPORT Human Clinical

"Cushing syndrome is associated with hyperglycemia, protein catabolism, immunosuppression, hypertension, weight gain, neurocognitive changes, and mood disorders."

This supports major metabolic overlap with diabetes mellitus, especially hyperglycemia.

acromegaly Not Yet Curated MONDO:0019933

Overlapping Features Growth hormone excess causes insulin resistance and secondary diabetes that can present similarly to type 2 diabetes.

Distinguishing Features

Acral/facial overgrowth and elevated GH/IGF-1 signaling indicate acromegaly-related secondary diabetes rather than primary diabetes subtypes.

Show evidence (1 reference)

PMID:36882643 SUPPORT Human Clinical

"Secondary diabetes mellitus (DM) is a common complication of acromegaly, encountered in up to 55% of cases."

This supports acromegaly as a clinically important endocrine differential when evaluating diabetes presentations.

drug-induced diabetes mellitus

Overlapping Features Pharmacotherapy-associated diabetes can mimic primary diabetes subtypes and may be transient or persistent depending on exposure and host factors.

Distinguishing Features

Temporal association with diabetogenic drug exposure and trajectory after dose reduction or withdrawal help distinguish this cause from primary subtype mechanisms.

Show evidence (2 references)

PMID:36106423 SUPPORT Human Clinical

"Drug-induced diabetes mellitus (DIDM) could be defined as a heterogenic group of diabetes caused by pharmacotherapy."

This supports drug-induced diabetes as a distinct etiologic category in differential diagnosis.

PMID:36106423 SUPPORT Human Clinical

This supports distinguishing features of reversibility and persistence risk relevant to causal attribution.

📊

Related Datasets

Metformin treatment effects on gut microbiome in T2D sra: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

Conditions: type 2 diabetes metformin treatment type 2 diabetes placebo

PMID:28530702

Nature Communications 2022 - metformin-microbiome interactions

Show evidence (1 reference)

PMID:28530702 SUPPORT Human Clinical

"In a double-blind study, we randomized individuals with treatment-naive T2D to placebo or metformin for 4 months and showed that metformin had strong effects on the gut microbiome."

This supports the metformin/placebo longitudinal gut microbiome dataset design captured by this accession.

Gut microbiome in urban African type 2 diabetes sra: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

Conditions: type 2 diabetes healthy controls

PMID:32158702

Frontiers Cellular Infection Microbiology 2020

Show evidence (1 reference)

PMID:32158702 SUPPORT Human Clinical

"Gut microbiota composition was determined in 291 Nigerians (98 cases, 193 controls) using fecal 16S V4 rRNA gene sequencing done on the Illumina MiSeq platform."

This supports an urban African case-control gut microbiome cohort in type 2 diabetes.

Gut microbiota in obese T2DM patients - Pakistani cohort sra: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

Conditions: obese type 2 diabetes healthy controls

PMID:31891582

Show evidence (1 reference)

PMID:31891582 SUPPORT Human Clinical

"The gut microbiota signature of 60 Pakistani adults was studied using 16S rRNA sequencing targeting V3-V4 hypervariable regions."

This supports the Pakistani obese-T2D versus healthy 16S dataset characteristics.

Chinese MGWAS of gut microbiome in type 2 diabetes sra: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

Conditions: type 2 diabetes healthy controls

PMID:23023125

Nature 2012 - first MGWAS of T2D, foundational study

Show evidence (1 reference)

PMID:23023125 SUPPORT Human Clinical

"we developed a protocol for a metagenome-wide association study (MGWAS) and undertook a two-stage MGWAS based on deep shotgun sequencing of the gut microbial DNA from 345 Chinese individuals."

This supports the Chinese MGWAS WGS cohort and sample count represented by this dataset.

🔬

Clinical Trials

NCT01030861 PHASE_II COMPLETED

Trial of teplizumab in high-risk relatives to prevent or delay progression to clinical type 1 diabetes.

Target Phenotypes: Hyperglycemia HP:0003074

Show evidence (1 reference)

clinicaltrials:NCT01030861 SUPPORT Human Clinical

"The study will determine whether the anti-CD3 monoclonal antibody, teplizumab, can help to prevent or delay the onset of type 1 diabetes (T1D) in relatives determined to be at very high risk for developing the disease."

This trial provides direct interventional evidence for delaying progression to clinical type 1 diabetes.

NCT03987919 PHASE_III COMPLETED

Phase 3 trial comparing tirzepatide versus semaglutide as add-on to metformin in type 2 diabetes.

Target Phenotypes: Hyperglycemia HP:0003074 Obesity HP:0001513

Show evidence (1 reference)

clinicaltrials:NCT03987919 SUPPORT Human Clinical

"The reason for this study is to compare the effect of the study drug tirzepatide to semaglutide on blood sugar levels in participants with type 2 diabetes."

This supports active late-phase comparative testing of incretin-based therapies for glycemic outcomes in type 2 diabetes.

NCT01131676 PHASE_III COMPLETED

Phase III cardiovascular safety study of BI 10773 in type 2 diabetes mellitus with increased cardiovascular risk.

Target Phenotypes: Hyperglycemia HP:0003074 Coronary artery atherosclerosis HP:0001677

Show evidence (1 reference)

clinicaltrials:NCT01131676 SUPPORT Human Clinical

"The aim of the present study is to investigate the safety of BI 10773 treatment in patients with Type 2 Diabetes Mellitus and high cardiovascular risk."

This supports large-scale cardiovascular-safety trial context in high-risk type 2 diabetes populations.

🧮

Computational Models

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

Show evidence (1 reference)

PMID:35276551 SUPPORT Computational

"The metabolic-flux profiles of pancreatic β-cells were predicted using genome-scale metabolic modeling for ten diabetic patients and ten control subjects."

This directly supports a pancreatic beta-cell genome-scale metabolic modeling resource in type 2 diabetes.

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:32463598 Base model: Recon3D

Predicts diabetes biomarkers and drug effects across multiple organs

Show evidence (1 reference)

PMID:32463598 SUPPORT Computational

"We developed a new metabolic network reconstruction approach that used organ-specific information from literature and omics data to generate two sex-specific whole-body metabolic (WBM) reconstructions."

This supports use of organ-resolved whole-body metabolic reconstructions for human systems modeling in diabetes-related contexts.

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:36658342

Nature Biotechnology 2022 - includes drug metabolism capabilities for 98 drugs; enables community-level FBA with MICOM

Show evidence (1 reference)

PMID:36658342 SUPPORT Computational

"AGORA2 (assembly of gut organisms through reconstruction and analysis, version 2) accounts for 7,302 strains, includes strain-resolved drug degradation and biotransformation capabilities for 98 drugs, and was extensively curated based on comparative genomics and literature searches."

This directly supports AGORA2 as a large-scale strain-resolved gut metabolic reconstruction resource.

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

Show evidence (1 reference)

PMID:31964767 SUPPORT Computational

"Here, we introduce MICOM, a customizable metabolic model of the human gut microbiome."

This directly supports MICOM as a metagenome-scale community metabolic modeling framework.

{ }

Source YAML

click to show

name: Diabetes mellitus
creation_date: "2026-02-21T15:39:12Z"
updated_date: "2026-05-08T20:47:43Z"
category: Complex
description: >-
  Diabetes mellitus is a heterogeneous group of disorders characterized by
  chronic hyperglycemia due to defects in insulin secretion, insulin action, or
  both.
disease_term:
  preferred_term: diabetes mellitus
  term:
    id: MONDO:0005015
    label: diabetes mellitus
parents:
- endocrine pancreas disorder
- glucose metabolism disease
classifications:
  harrisons_chapter:
  - classification_value: ENDOCRINOLOGY_METABOLISM
    evidence:
    - reference: PMID:26621825
      reference_title: "Pathophysiology of type 1 and type 2 diabetes mellitus: a 90-year perspective."
      supports: SUPPORT
      evidence_source: OTHER
      snippet: "Diabetes mellitus is a complex metabolic disorder associated with an increased risk of microvascular and macrovascular disease; its main clinical characteristic is hyperglycaemia."
      explanation: Supports assignment to the diabetes mellitus chapter-level classification.
definitions:
- name: Metabolic hyperglycemia case definition for diabetes mellitus
  definition_type: CASE_DEFINITION
  description: Diabetes mellitus is defined as a heterogeneous metabolic disease group with chronic hyperglycemia as the core clinical feature.
  scope: Cross-subtype disease-level framing
  evidence:
  - reference: PMID:26621825
    reference_title: "Pathophysiology of type 1 and type 2 diabetes mellitus: a 90-year perspective."
    supports: SUPPORT
    evidence_source: OTHER
    snippet: "Diabetes mellitus is a complex metabolic disorder associated with an increased risk of microvascular and macrovascular disease; its main clinical characteristic is hyperglycaemia."
    explanation: Supports a broad disease-level case framing centered on chronic hyperglycemia and vascular risk.
- name: USPSTF screening phenotype algorithm for prediabetes and type 2 diabetes
  definition_type: PHENOTYPE_ALGORITHM
  description: Screening-focused phenotype definition for identifying adults who should undergo testing for prediabetes and type 2 diabetes in primary care.
  scope: Asymptomatic nonpregnant adults in primary care settings
  inclusion_criteria:
  - preferred_term: Adults aged 35 to 70 years
    description: Age-based screening eligibility window used in the USPSTF recommendation.
  - preferred_term: Overweight or obesity
    term:
      id: HP:0001513
      label: Obesity
    description: Screening eligibility is defined for adults with overweight or obesity.
  evidence:
  - reference: PMID:34427594
    reference_title: "Screening for Prediabetes and Type 2 Diabetes: US Preventive Services Task Force Recommendation Statement."
    supports: SUPPORT
    evidence_source: OTHER
    snippet: "The USPSTF recommends screening for prediabetes and type 2 diabetes in adults aged 35 to 70 years who have overweight or obesity."
    explanation: Supports the population eligibility criteria in this screening algorithm definition.
  - reference: PMID:34427594
    reference_title: "Screening for Prediabetes and Type 2 Diabetes: US Preventive Services Task Force Recommendation Statement."
    supports: SUPPORT
    evidence_source: OTHER
    snippet: "Clinicians should offer or refer patients with prediabetes to effective preventive interventions."
    explanation: Supports the intervention linkage embedded in this screening algorithm framework.
has_subtypes:
- name: type 1 diabetes mellitus
  subtype_term:
    preferred_term: type 1 diabetes mellitus
    term:
      id: MONDO:0005147
      label: type 1 diabetes mellitus
  classification: mondo_direct_subclass
  description: Autoimmune-predominant diabetes with progressive beta-cell loss.
  children:
  - latent autoimmune diabetes in adults
  evidence:
  - reference: PMID:37960733
    reference_title: "Sintilimab-related diabetes mellitus and psoriasis: A case report and literature review."
    supports: PARTIAL
    evidence_source: HUMAN_CLINICAL
    snippet: "Most patients with immune checkpoint inhibitor-induced type 1 diabetes are reported to have no combination of autoimmune disease."
    explanation: This case report adds recent clinical context for an immune-therapy-associated type 1 diabetes presentation.
  - reference: PMID:34515603
    reference_title: "What Was Known About Childhood Diabetes Mellitus Before the Discovery of Insulin?"
    supports: PARTIAL
    evidence_source: HUMAN_CLINICAL
    snippet: "It is fortuitous that a 14 year old boy with what was unequivocally type 1 diabetes was selected to be the first insulin recipient, and the rest is history."
    explanation: This historical review provides context on early clinical recognition of unequivocal type 1 diabetes.
- name: latent autoimmune diabetes in adults
  subtype_term:
    preferred_term: latent autoimmune diabetes in adults
    term:
      id: MONDO:0850306
      label: latent autoimmune diabetes in adults
  classification: mondo_nested_subclass
  description: Adult-onset autoimmune diabetes within the type 1 spectrum.
- name: type 2 diabetes mellitus
  subtype_term:
    preferred_term: type 2 diabetes mellitus
    term:
      id: MONDO:0005148
      label: type 2 diabetes mellitus
  classification: mondo_direct_subclass
  description: Diabetes with dominant insulin resistance and relative insulin deficiency.
  children:
  - lipoatrophic diabetes
- name: lipoatrophic diabetes
  subtype_term:
    preferred_term: lipoatrophic diabetes
    term:
      id: MONDO:0005827
      label: lipoatrophic diabetes
  classification: mondo_nested_subclass
  description: Diabetes associated with lipodystrophy/lipoatrophy phenotypes.
- name: gestational diabetes
  subtype_term:
    preferred_term: gestational diabetes
    term:
      id: MONDO:0005406
      label: gestational diabetes
  classification: mondo_direct_subclass
  description: >-
    Pregnancy-associated diabetes characterized by glucose intolerance first
    emerging or first recognized during pregnancy, typically driven by
    pregnancy-induced insulin resistance with inadequate beta-cell compensation.
  evidence:
  - reference: PMID:33550962
    reference_title: "Gestational Diabetes Mellitus Pharmacological Prevention and Treatment."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Gestational diabetes mellitus (GDM) is one of the most common medical complications of pregnancy and is defined as glucose intolerance that first emerges or is first recognized during pregnancy."
    explanation: This supports the diagnostic framing of GDM as pregnancy-onset glucose intolerance.
  - reference: PMID:32679915
    reference_title: "Gestational Diabetes Mellitus: A Harbinger of the Vicious Cycle of Diabetes."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Gestational diabetes mellitus (GDM), characterized by a transitory form of diabetes induced by insulin resistance and pancreatic β-cell dysfunction during pregnancy, has been identified as one of the major obstacles in achieving improved maternal and child health."
    explanation: This supports core pathophysiologic features of gestational diabetes, including insulin resistance and beta-cell dysfunction.
  - reference: PMID:31345518
    reference_title: "Gestational Diabetes Mellitus."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Although it has been accepted for decades that women with gestational diabetes mellitus (GDM) are at high risk for future development of type 2 diabetes, vigorous debate regarding the value of detecting and treating GDM has persisted into the twenty-first century."
    explanation: This supports long-term progression risk after GDM, adding clinically relevant subtype context.
  - reference: PMID:34073737
    reference_title: "Metabolomic Biomarkers in Gestational Diabetes Mellitus: A Review of the Evidence."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Gestational diabetes mellitus (GDM) is the fastest growing type of diabetes, affecting between 2 to 38% of pregnancies worldwide, varying considerably depending on diagnostic criteria used and sample population studied."
    explanation: This supports the growing global burden and heterogeneity of GDM prevalence estimates.
- name: monogenic diabetes
  subtype_term:
    preferred_term: monogenic diabetes
    term:
      id: MONDO:0015967
      label: monogenic diabetes
  classification: mondo_direct_subclass
  description: >-
    Inherited single-gene forms of diabetes spanning neonatal diabetes, MODY
    subtypes, and related syndromic/mitochondrial presentations.
  children:
  - neonatal diabetes mellitus
  - maturity-onset diabetes of the young
  evidence:
  - reference: PMID:33409956
    reference_title: "Neonatal Diabetes Mellitus: Novel Mutations."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "OBJECTIVE: To describe the spectrum of neonatal diabetes mellitus (NDM), document new mutations, and review published Indian literature on the etiology of NDM."
    explanation: This supports inherited monogenic heterogeneity within early-onset diabetes presentations.
  - reference: PMID:33409956
    reference_title: "Neonatal Diabetes Mellitus: Novel Mutations."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "CONCLUSIONS: The authors describe 5 novel mutations, in the EIF2AK3, ABCC8, and GCK genes, a homozygous mutation at the ABCC8 locus presenting as TNDM, an obscure phenotype of the GCK gene mutation, and hyperglycemia-induced hemichorea-hemiballismus in a patient with TRMA."
    explanation: This supports diverse causative inherited variants in monogenic diabetes subtypes.
  - reference: PMID:29931562
    reference_title: "Monogenic Diabetes in Children and Adolescents: Recognition and Treatment Options."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Monogenic diabetes is due to single gene defects that primarily affect beta cell function with more than 30 different genes reported."
    explanation: This supports monogenic diabetes as a single-gene disease group with broad genetic heterogeneity.
  - reference: PMID:33046911
    reference_title: "Pathogenic variants in actionable MODY genes are associated with type 2 diabetes."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Here, we show a significant burden of pathogenic variants in genes linked with monogenic diabetes among people with common T2D, particularly in actionable MODY genes, thus implying that there should be a substantial change in care for carriers with T2D."
    explanation: This supports clinically relevant overlap between monogenic diabetes genes and common diabetes populations.
  - reference: PMID:35487478
    reference_title: "Contribution of rare variants in monogenic diabetes-genes to early-onset type 2 diabetes."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "CONCLUSION: Rare variants in monogenic-diabetes genes are associated with an increased risk of early-onset T2D, and interact with common T2D susceptibility variants in shaping it."
    explanation: This supports contribution of monogenic-diabetes genes to early-onset diabetes risk in broader populations.
  - reference: PMID:36178555
    reference_title: "Monogenic diabetes clinic (MDC): 3-year experience."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "AIM: In the pediatric diabetes clinic, patients with type 1 diabetes mellitus (T1D) account for more than 90% of cases, while monogenic forms represent about 6%."
    explanation: This supports non-trivial monogenic diabetes burden in pediatric clinical practice.
  - reference: PMID:36585034
    reference_title: "Co-segregation analysis and functional trial in vivo of candidate genes for monogenic diabetes."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Prior screening for autoimmune markers confirmed type 1 diabetes (T1D) diagnosis in 88.1% (n=1065) of patients, and targeted next-generation sequencing identified 3.5% (n=42) pathogenic variants in MODY genes."
    explanation: This supports detectability of pathogenic MODY-gene variants in real-world diabetes cohorts.
  - reference: PMID:31264968
    reference_title: "Residual β cell function and monogenic variants in long-duration type 1 diabetes patients."
    supports: PARTIAL
    evidence_source: HUMAN_CLINICAL
    snippet: "Genetic analysis revealed that 280 Medalists (27.5%) had monogenic diabetes variants; in 80 (7.9%) of these Medalists, the variants were classified as \"likely pathogenic\""
    explanation: This supports genetic heterogeneity and monogenic variant overlap among individuals with long-standing clinically diagnosed type 1 diabetes.
- name: neonatal diabetes mellitus
  subtype_term:
    preferred_term: neonatal diabetes mellitus
    term:
      id: MONDO:0016391
      label: neonatal diabetes mellitus
  classification: mondo_nested_subclass
  description: Monogenic diabetes presenting in early infancy.
  children:
  - transient neonatal diabetes mellitus
  - permanent neonatal diabetes mellitus
  evidence:
  - reference: PMID:33409956
    reference_title: "Neonatal Diabetes Mellitus: Novel Mutations."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "OBJECTIVE: To describe the spectrum of neonatal diabetes mellitus (NDM), document new mutations, and review published Indian literature on the etiology of NDM."
    explanation: This case-series report supports the heterogeneous clinical and genetic spectrum of neonatal diabetes mellitus.
  - reference: PMID:39344692
    reference_title: "Neonatal diabetes mellitus around the world: Update 2024."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Neonatal diabetes mellitus (NDM), defined as diabetes with an onset during the first 6 months of life, is a rare form of monogenic diabetes."
    explanation: This supports neonatal diabetes as a rare monogenic subtype with infancy-onset diagnostic framing.
  - reference: PMID:39344692
    reference_title: "Neonatal diabetes mellitus around the world: Update 2024."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "As a consequence, the list of NDM genes now exceeds 40."
    explanation: This supports extensive genetic heterogeneity within neonatal monogenic diabetes.
- name: transient neonatal diabetes mellitus
  subtype_term:
    preferred_term: transient neonatal diabetes mellitus
    term:
      id: MONDO:0020525
      label: transient neonatal diabetes mellitus
  classification: mondo_nested_subclass
  description: Neonatal diabetes with early remission and possible relapse later in life.
  evidence:
  - reference: PMID:33409956
    reference_title: "Neonatal Diabetes Mellitus: Novel Mutations."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Four patients had transient diabetes"
    explanation: This supports transient neonatal diabetes as a clinically observed subtype with identifiable monogenic etiologies.
- name: permanent neonatal diabetes mellitus
  subtype_term:
    preferred_term: permanent neonatal diabetes mellitus
    term:
      id: MONDO:0100164
      label: permanent neonatal diabetes mellitus
  classification: mondo_nested_subclass
  description: Persistent neonatal-onset diabetes requiring ongoing management.
  evidence:
  - reference: PMID:33409956
    reference_title: "Neonatal Diabetes Mellitus: Novel Mutations."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "7 had permanent diabetes"
    explanation: This supports permanent neonatal diabetes as a clinically observed subtype with diverse genetic causes.
- name: maturity-onset diabetes of the young
  subtype_term:
    preferred_term: maturity-onset diabetes of the young
    term:
      id: MONDO:0018911
      label: maturity-onset diabetes of the young
  classification: mondo_nested_subclass
  description: Monogenic diabetes group classically characterized by early-onset non-ketotic diabetes.
  children:
  - maturity-onset diabetes of the young type 1
  - maturity-onset diabetes of the young type 2
  - maturity-onset diabetes of the young type 3
  - maturity-onset diabetes of the young type 4
  - maturity-onset diabetes of the young type 6
  - maturity-onset diabetes of the young type 7
  - maturity-onset diabetes of the young type 8
  - maturity-onset diabetes of the young type 9
  - maturity-onset diabetes of the young type 10
  - maturity-onset diabetes of the young type 11
  - maturity-onset diabetes of the young, type 12
  - maturity-onset diabetes of the young type 13
  - maturity-onset diabetes of the young type 14
  - renal cysts and diabetes syndrome
  evidence:
  - reference: PMID:29931562
    reference_title: "Monogenic Diabetes in Children and Adolescents: Recognition and Treatment Options."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Now, MODY represents a clinically heterogeneous group of autosomal-dominant disorders caused by mutations in genes involved in beta cell development and insulin secretion and is the most common form of monogenic diabetes, estimated to account for 1–2% of diabetes cases (see Table 1) [13]."
    explanation: This supports MODY as a heterogeneous autosomal-dominant monogenic diabetes group.
- name: maturity-onset diabetes of the young type 1
  subtype_term:
    preferred_term: maturity-onset diabetes of the young type 1
    term:
      id: MONDO:0007452
      label: maturity-onset diabetes of the young type 1
  classification: mondo_nested_subclass
  description: MONDO-defined MODY subtype.
- name: maturity-onset diabetes of the young type 2
  subtype_term:
    preferred_term: maturity-onset diabetes of the young type 2
    term:
      id: MONDO:0007453
      label: maturity-onset diabetes of the young type 2
  classification: mondo_nested_subclass
  description: MONDO-defined MODY subtype.
- name: maturity-onset diabetes of the young type 3
  subtype_term:
    preferred_term: maturity-onset diabetes of the young type 3
    term:
      id: MONDO:0010894
      label: maturity-onset diabetes of the young type 3
  classification: mondo_nested_subclass
  description: MONDO-defined MODY subtype.
- name: maturity-onset diabetes of the young type 4
  subtype_term:
    preferred_term: maturity-onset diabetes of the young type 4
    term:
      id: MONDO:0011667
      label: maturity-onset diabetes of the young type 4
  classification: mondo_nested_subclass
  description: MONDO-defined MODY subtype.
- name: maturity-onset diabetes of the young type 6
  subtype_term:
    preferred_term: maturity-onset diabetes of the young type 6
    term:
      id: MONDO:0011668
      label: maturity-onset diabetes of the young type 6
  classification: mondo_nested_subclass
  description: MONDO-defined MODY subtype.
- name: maturity-onset diabetes of the young type 7
  subtype_term:
    preferred_term: maturity-onset diabetes of the young type 7
    term:
      id: MONDO:0012513
      label: maturity-onset diabetes of the young type 7
  classification: mondo_nested_subclass
  description: MONDO-defined MODY subtype.
- name: maturity-onset diabetes of the young type 8
  subtype_term:
    preferred_term: maturity-onset diabetes of the young type 8
    term:
      id: MONDO:0012348
      label: maturity-onset diabetes of the young type 8
  classification: mondo_nested_subclass
  description: MONDO-defined MODY subtype.
- name: maturity-onset diabetes of the young type 9
  subtype_term:
    preferred_term: maturity-onset diabetes of the young type 9
    term:
      id: MONDO:0012818
      label: maturity-onset diabetes of the young type 9
  classification: mondo_nested_subclass
  description: MONDO-defined MODY subtype.
- name: maturity-onset diabetes of the young type 10
  subtype_term:
    preferred_term: maturity-onset diabetes of the young type 10
    term:
      id: MONDO:0013240
      label: maturity-onset diabetes of the young type 10
  classification: mondo_nested_subclass
  description: MONDO-defined MODY subtype.
- name: maturity-onset diabetes of the young type 11
  subtype_term:
    preferred_term: maturity-onset diabetes of the young type 11
    term:
      id: MONDO:0013242
      label: maturity-onset diabetes of the young type 11
  classification: mondo_nested_subclass
  description: MONDO-defined MODY subtype.
- name: maturity-onset diabetes of the young, type 12
  subtype_term:
    preferred_term: maturity-onset diabetes of the young, type 12
    term:
      id: MONDO:0978299
      label: maturity-onset diabetes of the young, type 12
  classification: mondo_nested_subclass
  description: MONDO-defined MODY subtype.
- name: maturity-onset diabetes of the young type 13
  subtype_term:
    preferred_term: maturity-onset diabetes of the young type 13
    term:
      id: MONDO:0014589
      label: maturity-onset diabetes of the young type 13
  classification: mondo_nested_subclass
  description: MONDO-defined MODY subtype.
- name: maturity-onset diabetes of the young type 14
  subtype_term:
    preferred_term: maturity-onset diabetes of the young type 14
    term:
      id: MONDO:0014674
      label: maturity-onset diabetes of the young type 14
  classification: mondo_nested_subclass
  description: MONDO-defined MODY subtype.
- name: renal cysts and diabetes syndrome
  subtype_term:
    preferred_term: renal cysts and diabetes syndrome
    term:
      id: MONDO:0007669
      label: renal cysts and diabetes syndrome
  classification: mondo_nested_subclass
  description: Syndromic form overlapping MODY-related monogenic diabetes spectrum.
- name: maternally-inherited diabetes and deafness
  subtype_term:
    preferred_term: maternally-inherited diabetes and deafness
    term:
      id: MONDO:0010785
      label: maternally-inherited diabetes and deafness
  classification: mondo_direct_subclass
  description: Mitochondrial diabetes subtype associated with sensorineural hearing loss.
- name: type 5 diabetes mellitus
  subtype_term:
    preferred_term: type 5 diabetes mellitus
    term:
      id: MONDO:1010179
      label: type 5 diabetes mellitus
  classification: mondo_direct_subclass
  description: >-
    Recently formalized diabetes class (often framed as malnutrition-related
    diabetes) with insulin-deficient phenotypes in undernourished populations,
    while some contemporary literature frames overlapping cases as
    pancreatogenic fibro-inflammatory disease; classification boundaries remain
    under active debate.
  evidence:
  - reference: PMID:40657327
    reference_title: "Type 5 diabetes as a growing malnutrition driven health crisis in low and middle income countries."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Type 5 diabetes mellitus (T5DM), recently redefined as malnutrition-related diabetes, represents a distinct form of severe insulin-deficient diabetes that arises from chronic undernutrition, particularly during childhood and adolescence."
    explanation: This supports malnutrition-related etiology and insulin-deficient biology in type 5 diabetes.
  - reference: PMID:41180768
    reference_title: "The neglected epidemic of type 5 diabetes mellitus."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "It disproportionately affects malnourished teens in low- and middle-income countries and is therefore, also known as malnutrition-related diabetes."
    explanation: This supports demographic and epidemiologic context for type 5 diabetes burden.
  - reference: PMID:41456634
    reference_title: "Type 5 diabetes: A comprehensive review to understand the basis of diabetes of poverty."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Emerging evidence has revealed a unique pathophysiology, prompting the International Diabetes Federation (IDF) to officially recognize MRDM as \"Type 5 Diabetes\" (T5D)."
    explanation: This supports formal recognition and mechanistic distinction of type 5 diabetes in contemporary classification discourse.
  - reference: PMID:41675641
    reference_title: "Type 5 diabetes mellitus: redefining pancreatogenic diabetes through molecular, imaging, and AI-driven evidence."
    supports: PARTIAL
    evidence_source: OTHER
    snippet: "T5DM involves loss of insulin and glucagon alongside exocrine pancreatic insufficiency, malnutrition, and significant glycaemic variability."
    explanation: This review supports endocrine-exocrine dysfunction and glycemic instability in literature labeled as T5DM, while also reflecting pancreatogenic framing.
- name: diabetic ketoacidosis
  subtype_term:
    preferred_term: diabetic ketoacidosis
    term:
      id: MONDO:0012819
      label: diabetic ketoacidosis
  classification: mondo_direct_subclass
  description: >-
    Included here because MONDO currently places it as a direct subclass of
    diabetes mellitus, although it is often treated clinically as an acute
    complication state.
  evidence:
  - reference: PMID:37960733
    reference_title: "Sintilimab-related diabetes mellitus and psoriasis: A case report and literature review."
    supports: PARTIAL
    evidence_source: HUMAN_CLINICAL
    snippet: "Regrettably, the patient's personal decision to discontinue medication for a single day led to the emergence of acute ketoacidosis, coupled with a recurrence of psoriasis vulgaris."
    explanation: This case report provides real-world context for abrupt diabetic ketoacidosis decompensation.
prevalence:
- subtype: type 1 diabetes mellitus
  population: Global
  percentage: "2"
  notes: Approximate share of global diabetes burden attributed to type 1 diabetes.
  evidence:
  - reference: PMID:34599655
    reference_title: "Type 1 diabetes in 2017: global estimates of incident and prevalent cases in children and adults."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Globally, type 1 diabetes represents about 2% of the estimated total cases of diabetes, ranging from less than 1% in certain Pacific countries to more than 15% in Northern European populations in 2017."
    explanation: This supports the approximate global proportion of diabetes represented by type 1 diabetes.
- subtype: type 2 diabetes mellitus
  population: Global
  percentage: "90"
  notes: Type 2 diabetes represents the dominant share of global diabetes prevalence (at least 90% worldwide).
  evidence:
  - reference: PMID:16085737
    reference_title: "Diabetes and ethnic minorities."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Type 2 diabetes accounts for at least 90% of diabetes worldwide."
    explanation: This supports the dominant contribution of type 2 diabetes to global diabetes prevalence.
epidemiology:
- name: Cardiometabolic and hepatic comorbidity burden
  description: Diabetes is associated with increased cardiovascular and fatty liver disease risk in population-level U.S. epidemiologic data.
  factors:
  - cardiovascular disease risk
  - nonalcoholic fatty liver disease risk
  - nonalcoholic steatohepatitis risk
  evidence:
  - reference: PMID:34427594
    reference_title: "Screening for Prediabetes and Type 2 Diabetes: US Preventive Services Task Force Recommendation Statement."
    supports: SUPPORT
    evidence_source: OTHER
    snippet: "It is also associated with increased risks of cardiovascular disease, nonalcoholic fatty liver disease, and nonalcoholic steatohepatitis and was estimated to be the seventh leading cause of death in the US in 2017."
    explanation: This supports major comorbidity burden dimensions that should be considered in diabetes epidemiologic characterization.
progression:
- phase: Presymptomatic autoimmunity
  subtype: type 1 diabetes mellitus
  age_range: Childhood-Adolescence
  notes: Stage-based progression from islet autoimmunity and dysglycemia to symptomatic disease.
  evidence:
  - reference: PMID:26404926
    reference_title: "Staging presymptomatic type 1 diabetes: a scientific statement of JDRF, the Endocrine Society, and the American Diabetes Association."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Stage 3 represents manifestations of the typical clinical symptoms and signs of diabetes, which may include polyuria, polydipsia, weight loss, fatigue, diabetic ketoacidosis (DKA), and others."
    explanation: This supports staged progression of type 1 diabetes from presymptomatic phases to clinical onset.
- phase: Symptomatic onset
  subtype: type 1 diabetes mellitus
  age_range: Childhood-Adolescence
  evidence:
  - reference: PMID:33825933
    reference_title: "[Type 1 diabetes: an update]."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "The incidence of type 1 diabetes (T1D) has been rising steadily over the last 30 years, especially among children and adolescents, with the result that the number of cases in this age group doubles every 20 years."
    explanation: This supports frequent childhood/adolescent onset within type 1 diabetes progression.
- phase: Prediabetes progression
  subtype: type 2 diabetes mellitus
  age_range: Adolescence-Adulthood
  notes: Prediabetes can precede accelerated progression to overt type 2 diabetes in youth and adults.
  evidence:
  - reference: PMID:41595732
    reference_title: "The Natural History of Prediabetes and Cardiovascular Disease in the Pediatric Population."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Individuals with prediabetes are at a higher risk of developing youth-onset type 2 diabetes, which is considered a more aggressive form of the disease."
    explanation: This supports progression from prediabetes to overt type 2 diabetes, including aggressive youth-onset trajectories.
- phase: Pregnancy-onset dysglycemia
  subtype: gestational diabetes
  age_range: Pregnancy
  notes: GDM emerges during pregnancy in the setting of gestational insulin resistance and inadequate beta-cell compensation.
  evidence:
  - reference: PMID:33550962
    reference_title: "Gestational Diabetes Mellitus Pharmacological Prevention and Treatment."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Gestational diabetes mellitus (GDM) is one of the most common medical complications of pregnancy and is defined as glucose intolerance that first emerges or is first recognized during pregnancy."
    explanation: This supports pregnancy-onset dysglycemia as the defining progression phase of gestational diabetes.
- phase: Postpartum progression risk
  subtype: gestational diabetes
  age_range: Postpartum-Adulthood
  notes: Prior GDM confers elevated risk for future type 2 diabetes after pregnancy.
  evidence:
  - reference: PMID:31345518
    reference_title: "Gestational Diabetes Mellitus."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Although it has been accepted for decades that women with gestational diabetes mellitus (GDM) are at high risk for future development of type 2 diabetes, vigorous debate regarding the value of detecting and treating GDM has persisted into the twenty-first century."
    explanation: This supports postpartum progression risk from gestational diabetes to type 2 diabetes.
pathophysiology:
# The blended type-1/type-2/type-5 mechanism graph that previously lived here has
# been retired as part of the A2 diabetes refactor. Diabetes mellitus is modeled
# as: a Grouping over the distinct type entries (kb/groupings/Diabetes_Mellitus.yaml,
# owning MONDO:0005015) + the shared diabetic_vascular_complications mechanism
# module + the per-type standalone entries (Type_I_Diabetes, Type_2_Diabetes_Mellitus,
# Malnutrition-related_Diabetes_Mellitus). The type-specific routes to hyperglycemia
# are elaborated in those standalone entries; the shared complication cascade is the
# module. This umbrella retains the has_subtypes catalog, the case-definition/USPSTF
# screening definitions, and disease-level framing, and defers mechanism to the module
# via the single conforming node below. See docs/curation-notes/diabetes-mellitus-grouping-refactor.md.
- name: Chronic Hyperglycemia (shared diabetic complication cascade)
  description: >-
    Sustained hyperglycemia is the shared, subtype-independent driver of diabetic
    tissue injury across every form of diabetes mellitus. Whatever the upstream
    route (autoimmune beta-cell destruction in type 1, insulin resistance with
    beta-cell secretory failure in type 2, undernutrition-associated beta-cell
    impairment in type 5, or a monogenic beta-cell defect), chronic hyperglycemia
    feeds one conserved cascade: hyperglycemia-induced oxidative and AGE-RAGE
    stress, endothelial dysfunction and vascular inflammation, micro- and
    macrovascular injury, and diabetic end-organ complications (kidney disease,
    retinopathy, neuropathy, atherosclerotic cardiovascular disease). That cascade
    is captured once in the diabetic_vascular_complications module, to which this
    node conforms; the per-type entries elaborate their own routes to hyperglycemia.
  conforms_to: "diabetic_vascular_complications#Chronic Hyperglycemia"
  biological_processes:
  - preferred_term: glucose homeostasis
    term:
      id: GO:0042593
      label: glucose homeostasis
    modifier: ABNORMAL
  evidence:
  - reference: PMID:32872570
    reference_title: "Pathophysiology of Type 2 Diabetes Mellitus."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "According to the World Health Organization (WHO) diabetes mellitus is a chronic, metabolic disease characterized by elevated levels of blood glucose, which leads over time to damage to the heart, vasculature, eyes, kidneys and nerves."
    explanation: >-
      Supports chronic hyperglycemia as the shared driver of multiorgan diabetic
      complications, the mechanism captured by the diabetic_vascular_complications
      module this node conforms to.
phenotypes:
- name: Hyperglycemia
  category: Metabolic
  frequency: VERY_FREQUENT
  diagnostic: true
  description: Persistent elevation of blood glucose across diabetes subtypes.
  phenotype_term:
    preferred_term: Hyperglycemia
    term:
      id: HP:0003074
      label: Hyperglycemia
  evidence:
  - reference: PMID:39556629
    reference_title: "Diabetic Ketoacidosis: Evaluation and Treatment."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Traditionally, DKA has been diagnosed by the triad of hyperglycemia (blood glucose greater than 250 mg/dL), metabolic acidosis (pH less than 7.3, serum bicarbonate less than 18 mEq/L, anion gap greater than 10 mEq/L), and elevated serum (preferred) or urine ketones."
    explanation: This supports hyperglycemia as a core defining metabolic phenotype in decompensated diabetes presentations.
- name: Albuminuria
  category: Renal
  frequency: OCCASIONAL
  description: Persistent urinary albumin excretion reflecting diabetic glomerular and tubular injury.
  phenotype_term:
    preferred_term: Albuminuria
    term:
      id: HP:0012592
      label: Albuminuria
  evidence:
  - reference: PMID:41632731
    reference_title: "Diabetic Kidney Disease: From Pathophysiology To Treatment Perspectives."
    supports: SUPPORT
    evidence_source: OTHER
    snippet: "Changes in glomerular hemodynamics, glomerular and tubular hypertrophy, hyperfiltration, overactivation of the renin-angiotensin-aldosterone system (RAAS), podocyte injury, inflammation, oxidative stress, renal hypoxia, mitochondrial injury, and epigenetic changes are the main mechanisms leading to albuminuria, glomerulosclerosis and fibrosis."
    explanation: This directly supports albuminuria as a major renal phenotype in diabetic kidney disease pathways.
- name: Chronic kidney disease
  category: Renal
  frequency: OCCASIONAL
  description: Progressive reduction in kidney function due to cumulative diabetic renal injury.
  phenotype_term:
    preferred_term: Chronic kidney disease
    term:
      id: HP:0012622
      label: Chronic kidney disease
  evidence:
  - reference: PMID:41632731
    reference_title: "Diabetic Kidney Disease: From Pathophysiology To Treatment Perspectives."
    supports: SUPPORT
    evidence_source: OTHER
    snippet: "Diabetes mellitus (DM) is the most prevalent cause of CKD and end-stage kidney disease (ESKD), requiring renal replacement therapy (RRT)."
    explanation: This supports chronic kidney disease as a common and clinically important diabetes-associated phenotype.
  - reference: PMID:36619566
    reference_title: "Diabetes mellitus with a duration of 26 years combined with IgA nephropathy: A case report and literature review."
    supports: PARTIAL
    evidence_source: HUMAN_CLINICAL
    snippet: "Renal biopsy might aid in the definitive diagnosis of DKD, NDKD, and NDKD combined with DKD."
    explanation: This case report adds context that CKD in diabetes may include coexisting non-diabetic kidney disease.
- name: Diabetic retinopathy
  category: Ophthalmologic
  frequency: OCCASIONAL
  description: Progressive retinal microvascular injury related to chronic diabetes.
  phenotype_term:
    preferred_term: Retinopathy
    term:
      id: HP:0000488
      label: Retinopathy
  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: This supports diabetic retinopathy as a major microvascular phenotype contributing to vision-threatening outcomes.
- name: Diabetic neuropathy
  category: Neurological
  frequency: OCCASIONAL
  description: Peripheral and autonomic nerve injury associated with diabetic microvascular disease.
  phenotype_term:
    preferred_term: Peripheral neuropathy
    term:
      id: HP:0009830
      label: Peripheral neuropathy
  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: This supports diabetic neuropathy as a major microvascular phenotype in diabetes.
- name: Coronary artery disease
  category: Cardiovascular
  frequency: OCCASIONAL
  description: Atherosclerotic coronary artery narrowing that increases myocardial ischemic risk in diabetes.
  phenotype_term:
    preferred_term: Coronary artery atherosclerosis
    term:
      id: HP:0001677
      label: Coronary artery atherosclerosis
  evidence:
  - reference: PMID:39742220
    reference_title: "Diabetes Mellitus and Cardiovascular Disease: Exploring Epidemiology, Pathophysiology, and Treatment Strategies."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "The predominant form of CVD among diabetic patients is coronary artery disease (CAD), accounting for 29.4% of cases."
    explanation: This directly supports coronary artery disease as a dominant macrovascular phenotype in diabetes.
- name: Cerebrovascular disease
  category: Neurological
  frequency: OCCASIONAL
  description: Cerebral vascular disease burden with elevated ischemic stroke risk in diabetes.
  phenotype_term:
    preferred_term: Stroke
    term:
      id: HP:0001297
      label: Stroke
  evidence:
  - reference: PMID:32872570
    reference_title: "Pathophysiology of Type 2 Diabetes Mellitus."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "T2DM leads to a two- to four-fold increase in the mortality rate of adults from heart disease and stroke and is associated with both micro- and macro-vascular complications, the latter consisting of accelerated atherosclerosis leading to severe peripheral vascular disease, premature coronary artery disease (CAD) and increased risk of cerebrovascular diseases [178,179,180]."
    explanation: This supports increased cerebrovascular disease risk as a macrovascular phenotype in diabetes.
- name: Peripheral artery disease
  category: Cardiovascular
  frequency: OCCASIONAL
  description: Obstructive lower-limb arterial disease caused by accelerated peripheral atherosclerosis in diabetes.
  phenotype_term:
    preferred_term: Peripheral arterial stenosis
    term:
      id: HP:0004950
      label: Peripheral arterial stenosis
  evidence:
  - reference: PMID:32872570
    reference_title: "Pathophysiology of Type 2 Diabetes Mellitus."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "T2DM leads to a two- to four-fold increase in the mortality rate of adults from heart disease and stroke and is associated with both micro- and macro-vascular complications, the latter consisting of accelerated atherosclerosis leading to severe peripheral vascular disease, premature coronary artery disease (CAD) and increased risk of cerebrovascular diseases [178,179,180]."
    explanation: This supports peripheral artery disease as a major macrovascular complication phenotype in diabetes.
- name: Polyuria
  category: Renal
  frequency: FREQUENT
  description: Osmotic diuresis from hyperglycemia causes increased urine volume.
  phenotype_term:
    preferred_term: Polyuria
    term:
      id: HP:0000103
      label: Polyuria
  evidence:
  - reference: PMID:39556629
    reference_title: "Diabetic Ketoacidosis: Evaluation and Treatment."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Polyuria and polydipsia are the most common symptoms, followed by nausea, vomiting, abdominal pain, weight loss, severe fatigue, dyspnea, and preceding febrile illness."
    explanation: This supports polyuria as a common symptomatic phenotype in diabetic metabolic decompensation.
- name: Polydipsia
  category: Systemic
  frequency: FREQUENT
  description: Excessive thirst secondary to urinary water losses.
  phenotype_term:
    preferred_term: Polydipsia
    term:
      id: HP:0001959
      label: Polydipsia
  evidence:
  - reference: PMID:39556629
    reference_title: "Diabetic Ketoacidosis: Evaluation and Treatment."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Polyuria and polydipsia are the most common symptoms, followed by nausea, vomiting, abdominal pain, weight loss, severe fatigue, dyspnea, and preceding febrile illness."
    explanation: This supports polydipsia as a common symptomatic phenotype in diabetic metabolic decompensation.
- name: Weight loss
  category: Metabolic
  frequency: FREQUENT
  description: Catabolic insulin-deficient state at diabetes onset can cause unintentional weight loss.
  phenotype_term:
    preferred_term: Weight loss
    term:
      id: HP:0001824
      label: Weight loss
  evidence:
  - reference: PMID:35309606
    reference_title: "Type 1 diabetes mellitus in pediatric age group: A rising endemic."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Among clinical features at presentation, 56.5% presented with polyuria, 34.8% with polydipsia, 21.7% with polyphagia, 39.1% with weight loss."
    explanation: This supports weight loss as a common presenting phenotype in pediatric type 1 diabetes.
- name: Polyphagia
  category: Metabolic
  frequency: OCCASIONAL
  description: Increased appetite can occur in early insulin-deficient diabetes presentations.
  phenotype_term:
    preferred_term: Polyphagia
    term:
      id: HP:0002591
      label: Polyphagia
  evidence:
  - reference: PMID:35309606
    reference_title: "Type 1 diabetes mellitus in pediatric age group: A rising endemic."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Among clinical features at presentation, 56.5% presented with polyuria, 34.8% with polydipsia, 21.7% with polyphagia, 39.1% with weight loss."
    explanation: This supports polyphagia as a presenting phenotype in a subset of pediatric type 1 diabetes cases.
- name: Impaired glucose tolerance
  category: Metabolic
  frequency: VERY_FREQUENT
  description: Intermediate dysglycemia phenotype in prediabetes and early type 2 pathways.
  phenotype_term:
    preferred_term: Glucose intolerance
    term:
      id: HP:0001952
      label: Glucose intolerance
  evidence:
  - reference: PMID:34122344
    reference_title: "Advances in Screening, Early Diagnosis and Accurate Staging of Diabetic Neuropathy."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "On top of these staggering figures, are the number of people with impaired glucose tolerance (IGT) or metabolic syndrome with 373.9 million in 2019 (7.5%) and predicted rise to 548.4 million (8.6%) by 2045 (2)."
    explanation: This supports impaired glucose tolerance as a highly prevalent dysglycemic phenotype relevant to diabetes progression.
- name: Hyperinsulinemia
  category: Metabolic
  frequency: FREQUENT
  description: Compensatory early response to insulin resistance before beta-cell failure.
  phenotype_term:
    preferred_term: Hyperinsulinemia
    term:
      id: HP:0000842
      label: Hyperinsulinemia
  evidence:
  - reference: PMID:29939616
    reference_title: "Insulin Resistance."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Insulin resistance impairs glucose disposal, resulting in a compensatory increase in beta-cell insulin production and hyperinsulinemia."
    explanation: This supports hyperinsulinemia as a compensatory metabolic phenotype in insulin-resistant diabetes pathways.
- name: Obesity
  category: Metabolic
  frequency: FREQUENT
  description: Common comorbid phenotype that amplifies insulin resistance in type 2 pathways.
  phenotype_term:
    preferred_term: Obesity
    term:
      id: HP:0001513
      label: Obesity
  evidence:
  - reference: PMID:34427594
    reference_title: "Screening for Prediabetes and Type 2 Diabetes: US Preventive Services Task Force Recommendation Statement."
    supports: PARTIAL
    evidence_source: OTHER
    snippet: "The USPSTF recommends screening for prediabetes and type 2 diabetes in adults aged 35 to 70 years who have overweight or obesity."
    explanation: This supports obesity as a key and common risk-associated phenotype in type 2 diabetes pathways.
- name: Fatigue
  category: Systemic
  frequency: FREQUENT
  description: Common symptom associated with dysglycemia and impaired metabolic utilization.
  phenotype_term:
    preferred_term: Fatigue
    term:
      id: HP:0012378
      label: Fatigue
  evidence:
  - reference: PMID:39556629
    reference_title: "Diabetic Ketoacidosis: Evaluation and Treatment."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Polyuria and polydipsia are the most common symptoms, followed by nausea, vomiting, abdominal pain, weight loss, severe fatigue, dyspnea, and preceding febrile illness."
    explanation: This supports fatigue as a common symptomatic phenotype during diabetic ketoacidosis presentations.
- name: Diabetic ketoacidosis
  category: Endocrine
  frequency: OCCASIONAL
  description: Acute life-threatening decompensation from insulin deficiency and ketone excess.
  phenotype_term:
    preferred_term: Diabetic ketoacidosis
    term:
      id: HP:0001953
      label: Diabetic ketoacidosis
  evidence:
  - reference: PMID:39556629
    reference_title: "Diabetic Ketoacidosis: Evaluation and Treatment."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Diabetic ketoacidosis (DKA) is a life-threatening complication of type 1 and type 2 diabetes resulting from an absolute or relative insulin deficiency."
    explanation: This directly supports diabetic ketoacidosis as an established acute phenotype across diabetes subtypes.
  - reference: PMID:37960733
    reference_title: "Sintilimab-related diabetes mellitus and psoriasis: A case report and literature review."
    supports: PARTIAL
    evidence_source: HUMAN_CLINICAL
    snippet: "Regrettably, the patient's personal decision to discontinue medication for a single day led to the emergence of acute ketoacidosis, coupled with a recurrence of psoriasis vulgaris."
    explanation: This case report provides recent clinical context for acute ketoacidosis recurrence in an immune-therapy-associated diabetes presentation.
- name: Glycosuria
  category: Renal
  frequency: FREQUENT
  description: Urinary glucose loss when filtered glucose exceeds renal tubular reabsorption.
  phenotype_term:
    preferred_term: Glycosuria
    term:
      id: HP:0003076
      label: Glycosuria
  evidence:
  - reference: PMID:26621825
    reference_title: "Pathophysiology of type 1 and type 2 diabetes mellitus: a 90-year perspective."
    supports: PARTIAL
    evidence_source: OTHER
    snippet: "The central role of insulin in glucose metabolism regulation was clearly demonstrated during the early 1920s, when Banting, Best, Collip and Macleod successfully reduced blood glucose levels and glycosuria in a patient treated with a substance purified from bovine pancreata."
    explanation: This supports glycosuria as a clinically recognized glucose-related phenotype in diabetes history and pathophysiology.
biochemical:
- name: Blood glucose
  presence: Elevated
  frequency: VERY_FREQUENT
  evidence:
  - reference: PMID:6378696
    reference_title: "Pre-type I diabetes. Linear loss of beta cell response to intravenous glucose."
    supports: PARTIAL
    evidence_source: HUMAN_CLINICAL
    snippet: "Elevations of fasting blood glucose and peak glucose during oral glucose tolerance tests were not observed until the year before onset of clinically overt diabetes."
    explanation: This supports elevated blood glucose as a defining biochemical abnormality in progression to overt diabetes.
- name: Hemoglobin A1c (HbA1c)
  presence: Elevated
  frequency: VERY_FREQUENT
  evidence:
  - reference: PMID:22847316
    reference_title: "1,5-Anhydroglucitol in diabetes mellitus."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "The measure of glycated hemoglobin (HbA1c) concentration is the gold standard of glycemic control index in diabetes management and is well known as a marker for diabetes complications."
    explanation: This supports elevated HbA1c as a central biochemical marker in diabetes management and complication risk.
genetic:
- name: HLA-DQA1
  gene_term:
    preferred_term: HLA-DQA1
    term:
      id: hgnc:4942
      label: HLA-DQA1
  association: Susceptibility
  subtype: type 1 diabetes mellitus
  notes: Encodes the DQ alpha chain; susceptibility alleles include DQA1*05 (HLA-DQ2 context) and DQA1*03:01 (HLA-DQ8 context).
  evidence:
  - reference: PMID:22184118
    reference_title: "Type 1 diabetes-associated HLA-DQ8 transdimer accommodates a unique peptide repertoire."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "HLA-DQ2 and HLA-DQ8 are strongly predisposing haplotypes for type 1 diabetes (T1D)."
    explanation: This supports HLA-DQA1-containing DQ haplotypes as major susceptibility contributors in type 1 diabetes.
- name: HLA-DQB1
  gene_term:
    preferred_term: HLA-DQB1
    term:
      id: hgnc:4944
      label: HLA-DQB1
  association: Susceptibility
  subtype: type 1 diabetes mellitus
  notes: Encodes the DQ beta chain; susceptibility alleles include DQB1*02 (HLA-DQ2 context) and DQB1*03:02 (HLA-DQ8 context).
  evidence:
  - reference: PMID:22184118
    reference_title: "Type 1 diabetes-associated HLA-DQ8 transdimer accommodates a unique peptide repertoire."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "HLA-DQ2 and HLA-DQ8 are strongly predisposing haplotypes for type 1 diabetes (T1D)."
    explanation: This supports HLA-DQB1-containing DQ haplotypes as major susceptibility contributors in type 1 diabetes.
- name: INS
  gene_term:
    preferred_term: INS
    term:
      id: hgnc:6081
      label: INS
  association: Susceptibility
  subtype: type 1 diabetes mellitus
  notes: Insulin gene region variants influence autoimmune diabetes susceptibility.
  evidence:
  - reference: PMID:26404926
    reference_title: "Staging presymptomatic type 1 diabetes: a scientific statement of JDRF, the Endocrine Society, and the American Diabetes Association."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "The highest non-HLA genetic contribution arises from the INS, PTPN22, CTLA4, and IL2RA genes, with the latter three genes also contributing to susceptibility to other autoimmune diseases (17)."
    explanation: This supports INS as a major non-HLA susceptibility gene in type 1 diabetes.
- name: PTPN22
  gene_term:
    preferred_term: PTPN22
    term:
      id: hgnc:9652
      label: PTPN22
  association: Susceptibility
  subtype: type 1 diabetes mellitus
  evidence:
  - reference: PMID:26404926
    reference_title: "Staging presymptomatic type 1 diabetes: a scientific statement of JDRF, the Endocrine Society, and the American Diabetes Association."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "The highest non-HLA genetic contribution arises from the INS, PTPN22, CTLA4, and IL2RA genes, with the latter three genes also contributing to susceptibility to other autoimmune diseases (17)."
    explanation: This supports PTPN22 as a major non-HLA susceptibility gene in type 1 diabetes.
- name: IL2RA
  gene_term:
    preferred_term: IL2RA
    term:
      id: hgnc:6008
      label: IL2RA
  association: Susceptibility
  subtype: type 1 diabetes mellitus
  evidence:
  - reference: PMID:26404926
    reference_title: "Staging presymptomatic type 1 diabetes: a scientific statement of JDRF, the Endocrine Society, and the American Diabetes Association."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "The highest non-HLA genetic contribution arises from the INS, PTPN22, CTLA4, and IL2RA genes, with the latter three genes also contributing to susceptibility to other autoimmune diseases (17)."
    explanation: This supports IL2RA as a major non-HLA susceptibility gene in type 1 diabetes.
- name: CTLA4
  gene_term:
    preferred_term: CTLA4
    term:
      id: hgnc:2505
      label: CTLA4
  association: Susceptibility
  subtype: type 1 diabetes mellitus
  evidence:
  - reference: PMID:26404926
    reference_title: "Staging presymptomatic type 1 diabetes: a scientific statement of JDRF, the Endocrine Society, and the American Diabetes Association."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "The highest non-HLA genetic contribution arises from the INS, PTPN22, CTLA4, and IL2RA genes, with the latter three genes also contributing to susceptibility to other autoimmune diseases (17)."
    explanation: This supports CTLA4 as a major non-HLA susceptibility gene in type 1 diabetes.
- name: TCF7L2
  gene_term:
    preferred_term: TCF7L2
    term:
      id: hgnc:11641
      label: TCF7L2
  association: Risk factor
  subtype: type 2 diabetes mellitus
  evidence:
  - 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: "CDKN2A and CDKN2B, and confirm that variants near TCF7L2, SLC30A8, HHEX, FTO, PPARG, and KCNJ11 are associated with T2D risk."
    explanation: This supports TCF7L2 as an established type 2 diabetes risk locus.
- name: PPARG
  gene_term:
    preferred_term: PPARG
    term:
      id: hgnc:9236
      label: PPARG
  association: Risk factor
  subtype: type 2 diabetes mellitus
  evidence:
  - 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: "CDKN2A and CDKN2B, and confirm that variants near TCF7L2, SLC30A8, HHEX, FTO, PPARG, and KCNJ11 are associated with T2D risk."
    explanation: This supports PPARG as an established type 2 diabetes risk locus.
- name: KCNJ11
  gene_term:
    preferred_term: KCNJ11
    term:
      id: hgnc:6257
      label: KCNJ11
  association: Risk factor
  subtype: type 2 diabetes mellitus
  notes: Also implicated as a causative gene in monogenic neonatal diabetes in addition to common type 2 diabetes risk architecture.
  evidence:
  - 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: "CDKN2A and CDKN2B, and confirm that variants near TCF7L2, SLC30A8, HHEX, FTO, PPARG, and KCNJ11 are associated with T2D risk."
    explanation: This supports KCNJ11 as an established type 2 diabetes risk locus.
  - reference: PMID:33046911
    reference_title: "Pathogenic variants in actionable MODY genes are associated with type 2 diabetes."
    supports: PARTIAL
    evidence_source: HUMAN_CLINICAL
    snippet: "We show that, among 74,629 individuals, this burden is probably driven by the pathogenic variants found in GCK, and to a lesser extent in HNF4A, KCNJ11, HNF1B and ABCC8."
    explanation: This supports additional pathogenic-variant burden involving KCNJ11 in people labeled as common type 2 diabetes.
- name: SLC30A8
  gene_term:
    preferred_term: SLC30A8
    term:
      id: hgnc:20303
      label: SLC30A8
  association: Risk factor
  subtype: type 2 diabetes mellitus
  evidence:
  - 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: "CDKN2A and CDKN2B, and confirm that variants near TCF7L2, SLC30A8, HHEX, FTO, PPARG, and KCNJ11 are associated with T2D risk."
    explanation: This supports SLC30A8 as an established type 2 diabetes risk locus.
- name: HNF1A
  gene_term:
    preferred_term: HNF1A
    term:
      id: hgnc:11621
      label: HNF1A
  association: Causative
  subtype: monogenic diabetes
  notes: Canonical MODY3 gene with overlap between monogenic and multifactorial diabetes risk contexts.
  evidence:
  - reference: PMID:29931562
    reference_title: "Monogenic Diabetes in Children and Adolescents: Recognition and Treatment Options."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Although pathogenic mutations in any of the 14 genes may result in MODY, mutations in GCK, HNF1A, and HNF4A are the most common causes of MODY, representing 52, 10, and 32% of MODY cases in the UK, respectively [15, 33]."
    explanation: This supports HNF1A as a common causative monogenic diabetes gene within MODY.
  - reference: PMID:35299962
    reference_title: "HNF1A：From Monogenic Diabetes to Type 2 Diabetes and Gestational Diabetes Mellitus."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Some single nucleotide polymorphisms (SNPs) cause maturity-onset diabetes of the young type 3 (MODY3) while some others do not cause MODY3 but increase the susceptibility to T2D or GDM."
    explanation: This supports HNF1A variant-dependent effects across monogenic MODY and broader diabetes susceptibility.
  - reference: PMID:36178555
    reference_title: "Monogenic diabetes clinic (MDC): 3-year experience."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "When we considered probands of Group 1 with a parental history of hyperglycemia, 58.3% (21/36) had a positive genetic test for GCK or HNF1A genes"
    explanation: This supports frequent involvement of HNF1A in clinically selected pediatric monogenic diabetes evaluations.
- name: HNF4A
  gene_term:
    preferred_term: HNF4A
    term:
      id: hgnc:5024
      label: HNF4A
  association: Causative
  subtype: monogenic diabetes
  notes: Established MODY gene that also contributes pathogenic-variant burden in subsets of clinically diagnosed type 2 diabetes.
  evidence:
  - reference: PMID:29931562
    reference_title: "Monogenic Diabetes in Children and Adolescents: Recognition and Treatment Options."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Although pathogenic mutations in any of the 14 genes may result in MODY, mutations in GCK, HNF1A, and HNF4A are the most common causes of MODY, representing 52, 10, and 32% of MODY cases in the UK, respectively [15, 33]."
    explanation: This supports HNF4A as a common causative gene in monogenic MODY.
  - reference: PMID:33046911
    reference_title: "Pathogenic variants in actionable MODY genes are associated with type 2 diabetes."
    supports: PARTIAL
    evidence_source: HUMAN_CLINICAL
    snippet: "We show that, among 74,629 individuals, this burden is probably driven by the pathogenic variants found in GCK, and to a lesser extent in HNF4A, KCNJ11, HNF1B and ABCC8."
    explanation: This supports measurable HNF4A pathogenic-variant burden among people with common type 2 diabetes labels.
- name: HNF1B
  gene_term:
    preferred_term: HNF1B
    term:
      id: hgnc:11630
      label: HNF1B
  association: Causative
  subtype: monogenic diabetes
  notes: Causative gene in monogenic renal cysts and diabetes syndrome with frequent structural variants.
  evidence:
  - reference: PMID:29931562
    reference_title: "Monogenic Diabetes in Children and Adolescents: Recognition and Treatment Options."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "HNF1B mutations account for less than 10% of all MODY cases [62, 63]."
    explanation: This supports HNF1B as an established but less frequent monogenic MODY-related cause.
  - reference: PMID:33046911
    reference_title: "Pathogenic variants in actionable MODY genes are associated with type 2 diabetes."
    supports: PARTIAL
    evidence_source: HUMAN_CLINICAL
    snippet: "We show that, among 74,629 individuals, this burden is probably driven by the pathogenic variants found in GCK, and to a lesser extent in HNF4A, KCNJ11, HNF1B and ABCC8."
    explanation: This supports clinically relevant HNF1B pathogenic-variant burden within type 2 diabetes cohorts.
- name: ABCC8
  gene_term:
    preferred_term: ABCC8
    term:
      id: hgnc:59
      label: ABCC8
  association: Causative
  subtype: monogenic diabetes
  notes: ATP-sensitive potassium channel gene implicated in transient and permanent neonatal diabetes presentations.
  evidence:
  - reference: PMID:33409956
    reference_title: "Neonatal Diabetes Mellitus: Novel Mutations."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "2 with ABCC8 mutation"
    explanation: This supports ABCC8 as a causal gene in monogenic neonatal diabetes cases.
  - reference: PMID:33409956
    reference_title: "Neonatal Diabetes Mellitus: Novel Mutations."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "The authors describe 5 novel mutations, in the EIF2AK3, ABCC8, and GCK genes"
    explanation: This supports ABCC8 as a recurrently implicated monogenic diabetes gene.
  - reference: PMID:33046911
    reference_title: "Pathogenic variants in actionable MODY genes are associated with type 2 diabetes."
    supports: PARTIAL
    evidence_source: HUMAN_CLINICAL
    snippet: "We show that, among 74,629 individuals, this burden is probably driven by the pathogenic variants found in GCK, and to a lesser extent in HNF4A, KCNJ11, HNF1B and ABCC8."
    explanation: This supports ABCC8 pathogenic-variant burden in subsets of clinically common type 2 diabetes populations.
- name: GCK
  gene_term:
    preferred_term: GCK
    term:
      id: hgnc:4195
      label: GCK
  association: Causative
  subtype: monogenic diabetes
  notes: Glucokinase deficiency and mutation-associated phenotypes are represented in monogenic diabetes spectra.
  evidence:
  - reference: PMID:33409956
    reference_title: "Neonatal Diabetes Mellitus: Novel Mutations."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "2 siblings with complete glucokinase deficiency"
    explanation: This supports causal involvement of severe GCK defects in permanent neonatal diabetes.
  - reference: PMID:33409956
    reference_title: "Neonatal Diabetes Mellitus: Novel Mutations."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "The authors describe 5 novel mutations, in the EIF2AK3, ABCC8, and GCK genes"
    explanation: This supports GCK as a monogenic diabetes gene represented in clinically curated cases.
  - reference: PMID:33046911
    reference_title: "Pathogenic variants in actionable MODY genes are associated with type 2 diabetes."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "We show that, among 74,629 individuals, this burden is probably driven by the pathogenic variants found in GCK, and to a lesser extent in HNF4A, KCNJ11, HNF1B and ABCC8."
    explanation: This supports GCK as a major contributor to pathogenic monogenic-variant burden detected in type 2 diabetes cohorts.
  - reference: PMID:36178555
    reference_title: "Monogenic diabetes clinic (MDC): 3-year experience."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "When we considered probands of Group 1 with a parental history of hyperglycemia, 58.3% (21/36) had a positive genetic test for GCK or HNF1A genes"
    explanation: This supports frequent GCK detection in pediatric monogenic diabetes diagnostic pathways.
- name: EIF2AK3
  gene_term:
    preferred_term: EIF2AK3
    term:
      id: hgnc:3255
      label: EIF2AK3
  association: Causative
  subtype: monogenic diabetes
  notes: EIF2AK3-related Wolcott-Rallison syndrome is a recognized monogenic diabetes etiology.
  evidence:
  - reference: PMID:33409956
    reference_title: "Neonatal Diabetes Mellitus: Novel Mutations."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "The authors describe 5 novel mutations, in the EIF2AK3, ABCC8, and GCK genes"
    explanation: This supports EIF2AK3 as a causative gene in monogenic diabetes-related case series data.
- name: WFS1
  gene_term:
    preferred_term: WFS1
    term:
      id: hgnc:12762
      label: WFS1
  association: Causative
  subtype: monogenic diabetes
  notes: Wolfram syndrome gene associated with syndromic insulin-deficient diabetes presentations.
  evidence:
  - reference: PMID:28432734
    reference_title: "Monogenic diabetes syndromes: Locus-specific databases for Alström, Wolfram, and Thiamine-responsive megaloblastic anemia."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Pathogenic variants in WFS1 (MIM# 606201) cause Wolfram syndrome (WS) type 1, a rare neurodegenerative disease characterized by DM and optic atrophy (OA)."
    explanation: This supports WFS1 as a causative monogenic diabetes-syndrome gene.
- name: ALMS1
  gene_term:
    preferred_term: ALMS1
    term:
      id: hgnc:428
      label: ALMS1
  association: Causative
  subtype: monogenic diabetes
  notes: Syndromic monogenic diabetes gene in Alstrom syndrome.
  evidence:
  - reference: PMID:28432734
    reference_title: "Monogenic diabetes syndromes: Locus-specific databases for Alström, Wolfram, and Thiamine-responsive megaloblastic anemia."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Pathogenic variants in the gene ALMS1 (MIM# 606844) on chromosomes 2p13.1 have been identified in patients with Alström syndrome (AS), an autosomal‐recessive disease characterized by retinal dystrophy, childhood obesity, type 2 DM, and sensorineural hearing loss"
    explanation: This supports ALMS1 as a causative gene in syndromic monogenic diabetes.
- name: CISD2
  gene_term:
    preferred_term: CISD2
    term:
      id: hgnc:24212
      label: CISD2
  association: Causative
  subtype: monogenic diabetes
  notes: Wolfram syndrome type 2 gene within syndromic monogenic diabetes spectrum.
  evidence:
  - reference: PMID:28432734
    reference_title: "Monogenic diabetes syndromes: Locus-specific databases for Alström, Wolfram, and Thiamine-responsive megaloblastic anemia."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Pathogenic variants in the CISD2 gene (MIM# 611507) have been identified in patients with WS type 2"
    explanation: This supports CISD2 as a causative gene in a monogenic diabetes syndrome.
- name: SLC19A2
  gene_term:
    preferred_term: SLC19A2
    term:
      id: hgnc:10938
      label: SLC19A2
  association: Causative
  subtype: monogenic diabetes
  notes: Causative gene in thiamine-responsive megaloblastic anemia syndrome with early-onset nonautoimmune diabetes.
  evidence:
  - reference: PMID:28432734
    reference_title: "Monogenic diabetes syndromes: Locus-specific databases for Alström, Wolfram, and Thiamine-responsive megaloblastic anemia."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "The gene responsible, SLC19A2 (MIM# 603941), is located on chromosome 1q24.2, consists of six exons with 497 amino acids spanning a 22.5‐kb genomic region."
    explanation: This supports SLC19A2 as a causative gene in a syndromic monogenic diabetes form.
treatments:
- name: Insulin therapy
  description: Essential replacement therapy for insulin-deficient diabetes, and adjunctive therapy in advanced type 2 diabetes.
  treatment_term:
    preferred_term: insulin treatment
    term:
      id: NCIT:C179441
      label: Injected Insulin Diabetes Therapy
    therapeutic_agent:
    - preferred_term: insulin
      term:
        id: CHEBI:145810
        label: insulin
  evidence:
  - reference: PMID:33970586
    reference_title: "Diabetes: Type 1 Diabetes."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "The mainstay of management is a regimen of multiple daily injections of insulin or continuous subcutaneous insulin delivered via an insulin pump."
    explanation: This supports insulin replacement as core treatment in insulin-deficient diabetes pathways.
- name: Metformin
  description: First-line oral pharmacotherapy in type 2 diabetes to reduce hepatic glucose production and improve 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
  evidence:
  - reference: PMID:30150719
    reference_title: "Metformin reduces liver glucose production by inhibition of fructose-1-6-bisphosphatase."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "The biguanide drug metformin (N,N-dimethylbiguanide) works principally through inhibition of HGP, although enhanced glucose disposal has also been reported in some studies1."
    explanation: This supports metformin as a glucose-lowering therapy acting largely via suppression of hepatic glucose production.
- name: Dietary intervention
  description: Structured nutrition-focused intervention to improve glycemic control, weight trajectory, and cardiometabolic risk.
  therapeutic_modality: BEHAVIORAL
  treatment_term:
    preferred_term: dietary intervention
    term:
      id: NCIT:C15447
      label: Dietary Intervention
  evidence:
  - reference: PMID:38243953
    reference_title: "Missed Opportunities in Type 2 Diabetes Mellitus: A Narrative Review."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "The cornerstone of T2DM prevention is the control of modifiable risk factors and lifestyle changes to potentially prevent diabetes."
    explanation: This supports dietary intervention as a core modifiable lifestyle strategy in diabetes prevention and care.
  - reference: PMID:34427594
    reference_title: "Screening for Prediabetes and Type 2 Diabetes: US Preventive Services Task Force Recommendation Statement."
    supports: PARTIAL
    evidence_source: OTHER
    snippet: "Clinicians should offer or refer patients with prediabetes to effective preventive interventions."
    explanation: This supports evidence-based preventive lifestyle intervention programs relevant to dietary management.
- name: Physical activity increase
  description: Increased habitual and structured physical activity to improve insulin sensitivity and glycemic outcomes.
  treatment_term:
    preferred_term: aerobic exercise therapy
    term:
      id: NCIT:C15302
      label: Physical Therapy
  evidence:
  - reference: PMID:38243953
    reference_title: "Missed Opportunities in Type 2 Diabetes Mellitus: A Narrative Review."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "The cornerstone of T2DM prevention is the control of modifiable risk factors and lifestyle changes to potentially prevent diabetes."
    explanation: This supports physical-activity-focused lifestyle intervention as a core modifiable strategy in diabetes prevention and care.
  - reference: PMID:34427594
    reference_title: "Screening for Prediabetes and Type 2 Diabetes: US Preventive Services Task Force Recommendation Statement."
    supports: PARTIAL
    evidence_source: OTHER
    snippet: "Clinicians should offer or refer patients with prediabetes to effective preventive interventions."
    explanation: This supports referral to effective preventive intervention programs that include physical activity components.
- name: GLP-1 receptor agonists
  description: Incretin-based therapies that improve glycemic control and support weight reduction.
  treatment_term:
    preferred_term: targeted therapy
    term:
      id: NCIT:C93352
      label: Targeted Therapy
    therapeutic_agent:
    - preferred_term: semaglutide
      term:
        id: CHEBI:167574
        label: semaglutide
    - preferred_term: liraglutide
      term:
        id: CHEBI:71193
        label: liraglutide
  evidence:
  - reference: PMID:33068776
    reference_title: "GLP-1 receptor agonists in the treatment of type 2 diabetes - state-of-the-art."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "GLP-1RAs are recommended as the preferred first injectable glucose-lowering therapy for type 2 diabetes, even before insulin treatment."
    explanation: This supports GLP-1 receptor agonists as a recommended major therapy class in type 2 diabetes management.
  - reference: PMID:38639546
    reference_title: "Newer Pharmacologic Treatments in Adults With Type 2 Diabetes: A Clinical Guideline From the American College of Physicians."
    supports: SUPPORT
    evidence_source: OTHER
    snippet: "Use a GLP-1 agonist to reduce the risk for all-cause mortality, major adverse cardiovascular events, and stroke."
    explanation: The 2024 ACP clinical guideline on newer type 2 diabetes treatments recommends GLP-1 receptor agonists to reduce all-cause mortality, major adverse cardiovascular events, and stroke.
- name: SGLT2 inhibitors
  description: Oral agents that reduce plasma glucose by increasing urinary glucose excretion.
  treatment_term:
    preferred_term: Pharmacotherapy
    term:
      id: NCIT:C15986
      label: Pharmacotherapy
    therapeutic_agent:
    - preferred_term: empagliflozin
      term:
        id: CHEBI:82720
        label: empagliflozin
    - preferred_term: dapagliflozin
      term:
        id: CHEBI:85078
        label: dapagliflozin
    - preferred_term: canagliflozin
      term:
        id: CHEBI:73274
        label: canagliflozin
  evidence:
  - reference: PMID:33441402
    reference_title: "Sodium-glucose cotransporter protein-2 (SGLT-2) inhibitors and glucagon-like peptide-1 (GLP-1) receptor agonists for type 2 diabetes: systematic review and network meta-analysis of randomised controlled trials."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "In patients with type 2 diabetes, SGLT-2 inhibitors and GLP-1 receptor agonists reduced cardiovascular and renal outcomes, with some differences in benefits and harms."
    explanation: This supports SGLT2 inhibitors as an evidence-based therapy class with demonstrated outcome benefits in type 2 diabetes.
- name: GLP-1/GIP dual agonists
  description: Dual incretin receptor agonists (e.g., tirzepatide) for enhanced glycemic and weight outcomes.
  treatment_term:
    preferred_term: targeted therapy
    term:
      id: NCIT:C93352
      label: Targeted Therapy
    therapeutic_agent:
    - preferred_term: tirzepatide
      term:
        id: CHEBI:194186
        label: tirzepatide
  evidence:
  - reference: PMID:38831203
    reference_title: "GLP1-GIP receptor co-agonists: a promising evolution in the treatment of type 2 diabetes."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    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 supports dual incretin agonism as a treatment strategy in type 2 diabetes pathways.
- name: Teplizumab immunotherapy
  description: CD3-targeted immunotherapy to delay progression from stage 2 to stage 3 type 1 diabetes.
  treatment_term:
    preferred_term: immunotherapy
    term:
      id: NCIT:C15262
      label: Immunotherapy
    therapeutic_agent:
    - preferred_term: monoclonal antibody
      term:
        id: NCIT:C20401
        label: Monoclonal Antibody
  evidence:
  - reference: PMID:36877454
    reference_title: "Teplizumab: First Approval."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "In November 2022, teplizumab was approved in the USA to delay the onset of Stage 3 T1D in adults and pediatric patients 8 years of age and older with Stage 2 T1D"
    explanation: This supports teplizumab as subtype-specific disease-modifying therapy in early type 1 diabetes.
- name: Blood glucose monitoring
  description: Self-monitoring of blood glucose and ketones when indicated to guide day-to-day therapeutic adjustments.
  evidence:
  - reference: PMID:32256447
    reference_title: "Monitoring of Pediatric Type 1 Diabetes."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Regular self-monitoring of blood glucose levels, and ketones when indicated, is an essential component of type 1 diabetes (T1D) management."
    explanation: This supports frequent glucose monitoring as a core management component.
- name: Continuous glucose monitoring
  description: Real-time sensor-based glycemia tracking that improves time-in-range and HbA1c outcomes.
  evidence:
  - reference: PMID:34872983
    reference_title: "Universal Subsidized Continuous Glucose Monitoring Funding for Young People With Type 1 Diabetes: Uptake and Outcomes Over 2 Years, a Population-Based Study."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "After CGM introduction, the odds ratio (OR) of achieving the HbA1c target of <7.0% improved at 12 months (OR 2.5, P < 0.001) and was maintained at 24 months"
    explanation: This supports CGM use for improved long-term glycemic target attainment.
differential_diagnoses:
- name: diabetes insipidus
  description: Polyuria-polydipsia syndrome that can mimic symptomatic presentations of diabetes mellitus.
  distinguishing_features:
  - Diabetes insipidus produces large volumes of dilute urine from AVP-axis dysfunction rather than persistent hyperglycemia-driven osmotic diuresis.
  disease_term:
    preferred_term: diabetes insipidus
    term:
      id: MONDO:0004782
      label: diabetes insipidus
  evidence:
  - reference: PMID:27156759
    reference_title: "Diabetes insipidus: Differential diagnosis and management."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Diabetes insipidus (DI) is a syndrome characterized by the excretion of abnormally large volumes of dilute urine."
    explanation: This supports the key overlapping presentation of marked polyuria that often enters the differential of diabetes symptoms.
  - reference: PMID:39556629
    reference_title: "Diabetic Ketoacidosis: Evaluation and Treatment."
    supports: PARTIAL
    evidence_source: HUMAN_CLINICAL
    snippet: "Polyuria and polydipsia are the most common symptoms, followed by nausea, vomiting, abdominal pain, weight loss, severe fatigue, dyspnea, and preceding febrile illness."
    explanation: This supports symptom overlap (polyuria/polydipsia) with diabetes mellitus presentations.
- name: primary polydipsia
  description: Excessive fluid intake syndrome in which polydipsia and polyuria can be confused with diabetes-related symptoms.
  distinguishing_features:
  - Primary polydipsia is driven by excessive fluid intake with AVP suppression and lacks persistent diabetes pathophysiology.
  disease_term:
    preferred_term: primary polydipsia
    term:
      id: MONDO:0040870
      label: primary polydipsia
  evidence:
  - reference: PMID:27156759
    reference_title: "Diabetes insipidus: Differential diagnosis and management."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "primary polydipsia, due to suppression of AVP secretion by excessive fluid intake;"
    explanation: This supports primary polydipsia as a distinct non-diabetes cause of polyuria-polydipsia.
  - reference: PMID:39556629
    reference_title: "Diabetic Ketoacidosis: Evaluation and Treatment."
    supports: PARTIAL
    evidence_source: HUMAN_CLINICAL
    snippet: "Polyuria and polydipsia are the most common symptoms, followed by nausea, vomiting, abdominal pain, weight loss, severe fatigue, dyspnea, and preceding febrile illness."
    explanation: This supports symptom overlap that can create diagnostic confusion with diabetes mellitus.
- name: Cushing syndrome
  description: Hypercortisolism can present with hyperglycemia and metabolic findings that overlap with diabetes mellitus.
  distinguishing_features:
  - Cushingoid physical findings and biochemical cortisol-axis testing help distinguish this endocrine disorder from primary diabetes categories.
  disease_term:
    preferred_term: Cushing syndrome
    term:
      id: MONDO:0018912
      label: Cushing syndrome
  evidence:
  - reference: PMID:37432427
    reference_title: "Cushing Syndrome: A Review."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Cushing syndrome is associated with hyperglycemia, protein catabolism, immunosuppression, hypertension, weight gain, neurocognitive changes, and mood disorders."
    explanation: This supports major metabolic overlap with diabetes mellitus, especially hyperglycemia.
- name: acromegaly
  description: Growth hormone excess causes insulin resistance and secondary diabetes that can present similarly to type 2 diabetes.
  distinguishing_features:
  - Acral/facial overgrowth and elevated GH/IGF-1 signaling indicate acromegaly-related secondary diabetes rather than primary diabetes subtypes.
  disease_term:
    preferred_term: acromegaly
    term:
      id: MONDO:0019933
      label: acromegaly
  evidence:
  - reference: PMID:36882643
    reference_title: "Secondary diabetes mellitus in acromegaly."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Secondary diabetes mellitus (DM) is a common complication of acromegaly, encountered in up to 55% of cases."
    explanation: This supports acromegaly as a clinically important endocrine differential when evaluating diabetes presentations.
- name: drug-induced diabetes mellitus
  description: Pharmacotherapy-associated diabetes can mimic primary diabetes subtypes and may be transient or persistent depending on exposure and host factors.
  distinguishing_features:
  - Temporal association with diabetogenic drug exposure and trajectory after dose reduction or withdrawal help distinguish this cause from primary subtype mechanisms.
  evidence:
  - reference: PMID:36106423
    reference_title: "Atypical diabetes mellitus in children - when to suspect drug-induced diabetes. A case-based review of the literature."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Drug-induced diabetes mellitus (DIDM) could be defined as a heterogenic group of diabetes caused by pharmacotherapy."
    explanation: This supports drug-induced diabetes as a distinct etiologic category in differential diagnosis.
  - reference: PMID:36106423
    reference_title: "Atypical diabetes mellitus in children - when to suspect drug-induced diabetes. A case-based review of the literature."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "The DIDM is considered to be reversible after discontinuation of diabetogenic treatment, but there is a risk of persistence, which is related to the duration of treatment, prescribed medication, and body mass index."
    explanation: This supports distinguishing features of reversibility and persistence risk relevant to causal attribution.
clinical_trials:
- name: NCT01030861
  phase: PHASE_II
  status: COMPLETED
  description: Trial of teplizumab in high-risk relatives to prevent or delay progression to clinical type 1 diabetes.
  target_phenotypes:
  - preferred_term: Hyperglycemia
    term:
      id: HP:0003074
      label: Hyperglycemia
  evidence:
  - reference: clinicaltrials:NCT01030861
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "The study will determine whether the anti-CD3 monoclonal antibody, teplizumab, can help to prevent or delay the onset of type 1 diabetes (T1D) in relatives determined to be at very high risk for developing the disease."
    explanation: This trial provides direct interventional evidence for delaying progression to clinical type 1 diabetes.
- name: NCT03987919
  phase: PHASE_III
  status: COMPLETED
  description: Phase 3 trial comparing tirzepatide versus semaglutide as add-on to metformin in type 2 diabetes.
  target_phenotypes:
  - preferred_term: Hyperglycemia
    term:
      id: HP:0003074
      label: Hyperglycemia
  - preferred_term: Obesity
    term:
      id: HP:0001513
      label: Obesity
  evidence:
  - reference: clinicaltrials:NCT03987919
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "The reason for this study is to compare the effect of the study drug tirzepatide to semaglutide on blood sugar levels in participants with type 2 diabetes."
    explanation: This supports active late-phase comparative testing of incretin-based therapies for glycemic outcomes in type 2 diabetes.
- name: NCT01131676
  phase: PHASE_III
  status: COMPLETED
  description: Phase III cardiovascular safety study of BI 10773 in type 2 diabetes mellitus with increased cardiovascular risk.
  target_phenotypes:
  - preferred_term: Hyperglycemia
    term:
      id: HP:0003074
      label: Hyperglycemia
  - preferred_term: Coronary artery atherosclerosis
    term:
      id: HP:0001677
      label: Coronary artery atherosclerosis
  evidence:
  - reference: clinicaltrials:NCT01131676
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "The aim of the present study is to investigate the safety of BI 10773 treatment in patients with Type 2 Diabetes Mellitus and high cardiovascular risk."
    explanation: This supports large-scale cardiovascular-safety trial context in high-risk type 2 diabetes populations.
datasets:
- accession: sra: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
    tissue_term:
      preferred_term: feces
      term:
        id: UBERON:0001988
        label: feces
  sample_count: 40
  conditions:
  - type 2 diabetes metformin treatment
  - type 2 diabetes placebo
  publication: PMID:28530702
  evidence:
  - reference: PMID:28530702
    reference_title: "Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "In a double-blind study, we randomized individuals with treatment-naive T2D to placebo or metformin for 4 months and showed that metformin had strong effects on the gut microbiome."
    explanation: This supports the metformin/placebo longitudinal gut microbiome dataset design captured by this accession.
  notes: Nature Communications 2022 - metformin-microbiome interactions
- accession: sra: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
    tissue_term:
      preferred_term: feces
      term:
        id: UBERON:0001988
        label: feces
  conditions:
  - type 2 diabetes
  - healthy controls
  publication: PMID:32158702
  evidence:
  - reference: PMID:32158702
    reference_title: "Gut Microbiome Profiles Are Associated With Type 2 Diabetes in Urban Africans."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Gut microbiota composition was determined in 291 Nigerians (98 cases, 193 controls) using fecal 16S V4 rRNA gene sequencing done on the Illumina MiSeq platform."
    explanation: This supports an urban African case-control gut microbiome cohort in type 2 diabetes.
  notes: Frontiers Cellular Infection Microbiology 2020
- accession: sra: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
    tissue_term:
      preferred_term: feces
      term:
        id: UBERON:0001988
        label: feces
  sample_count: 60
  conditions:
  - obese type 2 diabetes
  - healthy controls
  publication: PMID:31891582
  evidence:
  - reference: PMID:31891582
    reference_title: "Analysis of gut microbiota of obese individuals with type 2 diabetes and healthy individuals."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "The gut microbiota signature of 60 Pakistani adults was studied using 16S rRNA sequencing targeting V3-V4 hypervariable regions."
    explanation: This supports the Pakistani obese-T2D versus healthy 16S dataset characteristics.
- accession: sra: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
    tissue_term:
      preferred_term: feces
      term:
        id: UBERON:0001988
        label: feces
  sample_count: 345
  conditions:
  - type 2 diabetes
  - healthy controls
  publication: PMID:23023125
  evidence:
  - reference: PMID:23023125
    reference_title: "A metagenome-wide association study of gut microbiota in type 2 diabetes."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "we developed a protocol for a metagenome-wide association study (MGWAS) and undertook a two-stage MGWAS based on deep shotgun sequencing of the gut microbial DNA from 345 Chinese individuals."
    explanation: This supports the Chinese MGWAS WGS cohort and sample count represented by this dataset.
  notes: Nature 2012 - first MGWAS of T2D, foundational study
computational_models:
- 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
  evidence:
  - reference: PMID:35276551
    reference_title: "Elucidating the metabolic characteristics of pancreatic β-cells from patients with type 2 diabetes (T2D) using a genome-scale metabolic modeling."
    supports: SUPPORT
    evidence_source: COMPUTATIONAL
    snippet: "The metabolic-flux profiles of pancreatic β-cells were predicted using genome-scale metabolic modeling for ten diabetic patients and ten control subjects."
    explanation: This directly supports a pancreatic beta-cell genome-scale metabolic modeling resource in type 2 diabetes.
- 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:32463598
  notes: Predicts diabetes biomarkers and drug effects across multiple organs
  evidence:
  - reference: PMID:32463598
    reference_title: "Personalized whole-body models integrate metabolism, physiology, and the gut microbiome."
    supports: SUPPORT
    evidence_source: COMPUTATIONAL
    snippet: "We developed a new metabolic network reconstruction approach that used organ-specific information from literature and omics data to generate two sex-specific whole-body metabolic (WBM) reconstructions."
    explanation: This supports use of organ-resolved whole-body metabolic reconstructions for human systems modeling in diabetes-related contexts.
- 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:36658342
  notes: Nature Biotechnology 2022 - includes drug metabolism capabilities for 98 drugs; enables community-level FBA with MICOM
  evidence:
  - reference: PMID:36658342
    reference_title: "Genome-scale metabolic reconstruction of 7,302 human microorganisms for personalized medicine."
    supports: SUPPORT
    evidence_source: COMPUTATIONAL
    snippet: "AGORA2 (assembly of gut organisms through reconstruction and analysis, version 2) accounts for 7,302 strains, includes strain-resolved drug degradation and biotransformation capabilities for 98 drugs, and was extensively curated based on comparative genomics and literature searches."
    explanation: This directly supports AGORA2 as a large-scale strain-resolved gut metabolic reconstruction resource.
- 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
  evidence:
  - reference: PMID:31964767
    reference_title: "MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota."
    supports: SUPPORT
    evidence_source: COMPUTATIONAL
    snippet: "Here, we introduce MICOM, a customizable metabolic model of the human gut microbiome."
    explanation: This directly supports MICOM as a metagenome-scale community metabolic modeling framework.
environmental:
- name: Environmental determinants of diabetes susceptibility and progression
  description: >-
    Environmental contexts modify susceptibility and progression across diabetes
    subtypes rather than a single uniform etiologic pathway.
  effect: Alters diabetes risk trajectories and complication burden across subtypes.
  evidence:
  - reference: PMID:27980006
    reference_title: "Differentiation of Diabetes by Pathophysiology, Natural History, and Prognosis."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "International experts in genetics, immunology, metabolism, endocrinology, and systems biology discussed genetic and environmental determinants of type 1 and type 2 diabetes risk and progression, as well as complications."
    explanation: This supports broad environmental contributions to diabetes susceptibility and progression.
- name: Exogenous triggers of islet autoimmunity in type 1 diabetes pathways
  description: >-
    In genetically predisposed individuals, exogenous exposures can initiate
    autoimmune processes targeting pancreatic beta cells.
  effect: Promotes onset of autoimmune beta-cell injury and progression to clinical type 1 diabetes.
  evidence:
  - reference: PMID:10522815
    reference_title: "Environmental factors in the pathogenesis of type 1 diabetes mellitus."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Various exogenous triggers, such as certain dietary factors and viruses, are thought to induce the autoimmune process leading in some individuals to extensive beta-cell destruction and ultimately to the clinical manifestation of type 1 diabetes."
    explanation: This supports environmental exposure-triggered autoimmunity as a key risk relationship in type 1 diabetes biology.
- name: Diabetogenic pharmacotherapy exposure
  description: Exposure to medications with diabetogenic effects can precipitate persistent or transient diabetes phenotypes.
  effect: Increases risk of treatment-associated hyperglycemia and overt diabetes in susceptible patients.
  evidence:
  - reference: PMID:36106423
    reference_title: "Atypical diabetes mellitus in children - when to suspect drug-induced diabetes. A case-based review of the literature."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Drug-induced diabetes mellitus (DIDM) could be defined as a heterogenic group of diabetes caused by pharmacotherapy."
    explanation: This case-based review supports medication exposure as an environmental contributor to diabetes onset.
  - reference: PMID:36106423
    reference_title: "Atypical diabetes mellitus in children - when to suspect drug-induced diabetes. A case-based review of the literature."
    supports: PARTIAL
    evidence_source: HUMAN_CLINICAL
    snippet: "The DIDM is considered to be reversible after discontinuation of diabetogenic treatment, but there is a risk of persistence, which is related to the duration of treatment, prescribed medication, and body mass index."
    explanation: This adds context on heterogeneity and persistence risk in pharmacotherapy-associated diabetes.
- name: Immune checkpoint inhibitor exposure
  description: Immune checkpoint inhibitor therapy can trigger insulin-deficient diabetes with abrupt decompensation in some patients.
  effect: Can precipitate immune-related diabetes presentations, including ketoacidosis.
  evidence:
  - reference: PMID:37960733
    reference_title: "Sintilimab-related diabetes mellitus and psoriasis: A case report and literature review."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "The clinical manifestations of immune checkpoint inhibitor induced diabetes mellitus are variable, and in this case the patient presented with unique primary symptoms."
    explanation: This case report supports checkpoint inhibitor exposure as a distinct treatment-associated diabetes trigger context.
- name: Viral infection exposure
  description: Enteroviral and other viral exposures are implicated as triggers of islet autoimmunity in susceptible individuals.
  effect: Increases risk of autoimmune beta-cell injury and progression to clinical type 1 diabetes.
  exposure_term:
    preferred_term: Viral infection exposure
    term:
      id: ECTO:3000001
      label: exposure to virus
  evidence:
  - reference: PMID:10522815
    reference_title: "Environmental factors in the pathogenesis of type 1 diabetes mellitus."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Various exogenous triggers, such as certain dietary factors and viruses, are thought to induce the autoimmune process leading in some individuals to extensive beta-cell destruction and ultimately to the clinical manifestation of type 1 diabetes."
    explanation: This supports viral exposure as an environmental trigger linked to autoimmune diabetes onset.
- name: Early dietary exposures
  description: Early-life dietary exposures (e.g., infant feeding patterns and specific food antigens) are investigated as autoimmune-modulating factors.
  effect: May modulate risk of islet autoimmunity in genetically susceptible populations.
  evidence:
  - reference: PMID:10522815
    reference_title: "Environmental factors in the pathogenesis of type 1 diabetes mellitus."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Various exogenous triggers, such as certain dietary factors and viruses, are thought to induce the autoimmune process leading in some individuals to extensive beta-cell destruction and ultimately to the clinical manifestation of type 1 diabetes."
    explanation: This supports early dietary exposures as environmental modulators of autoimmune risk in susceptible individuals.
- name: Sedentary lifestyle
  description: Low physical activity patterns contribute to insulin resistance and cardiometabolic risk amplification.
  effect: Increases progression risk toward type 2 diabetes trajectories.
  evidence:
  - reference: PMID:41632731
    reference_title: "Diabetic Kidney Disease: From Pathophysiology To Treatment Perspectives."
    supports: SUPPORT
    evidence_source: OTHER
    snippet: "Type II DM remains the most prevalent subtype, as it is closely related to metabolic syndrome, whose prevalence is also rising with a sedentary lifestyle and Western diet."
    explanation: This supports sedentary lifestyle as an environmental contributor linked to type 2 diabetes epidemiology and progression.
- name: High-calorie diet
  description: Energy-dense dietary patterns promote positive energy balance, adiposity, and insulin resistance.
  effect: Accelerates progression from metabolic risk states to overt type 2 diabetes.
  evidence:
  - reference: PMID:29939616
    reference_title: "Insulin Resistance."
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "This concept may be clinically valuable, suggesting that hyperinsulinemia associated with excess caloric intake may drive the metabolic dysfunction associated with insulin resistance."
    explanation: This supports excess caloric intake as an environmental dietary driver of insulin-resistant diabetes pathways.
- name: Obesogenic environment
  description: Environmental contexts favoring obesity (dietary excess, low activity, and social determinants) amplify diabetes susceptibility.
  effect: Increases insulin resistance burden and long-term diabetes complication risk.
  evidence:
  - reference: PMID:34427594
    reference_title: "Screening for Prediabetes and Type 2 Diabetes: US Preventive Services Task Force Recommendation Statement."
    supports: PARTIAL
    evidence_source: OTHER
    snippet: "The USPSTF recommends screening for prediabetes and type 2 diabetes in adults aged 35 to 70 years who have overweight or obesity."
    explanation: This supports obesity-promoting environmental context as a major risk-linked feature in type 2 diabetes prevention and care.
references:
- reference: DOI:10.1186/s12916-023-02851-5
  title: 'Dose–response relationship between device-measured physical activity and incident type 2 diabetes: findings from the UK Biobank prospective cohort study'
  found_in:
  - Diabetes_Mellitus-deep-research-falcon.md
  findings:
  - statement: Most studies investigating the association between physical activity (PA) and the risk of type 2 diabetes are derived from self-reported questionnaires, with limited evidence using device-based measurements.
    supporting_text: Most studies investigating the association between physical activity (PA) and the risk of type 2 diabetes are derived from self-reported questionnaires, with limited evidence using device-based measurements.
    evidence:
    - reference: DOI:10.1186/s12916-023-02851-5
      reference_title: 'Dose–response relationship between device-measured physical activity and incident type 2 diabetes: findings from the UK Biobank prospective cohort study'
      supports: SUPPORT
      evidence_source: HUMAN_CLINICAL
      snippet: Most studies investigating the association between physical activity (PA) and the risk of type 2 diabetes are derived from self-reported questionnaires, with limited evidence using device-based measurements.
      explanation: Deep research cited this publication as relevant literature for Diabetes Mellitus.
- reference: DOI:10.1186/s12933-024-02154-w
  title: 'The effectiveness of sodium-glucose co-transporter 2 inhibitors on cardiorenal outcomes: an updated systematic review and meta-analysis'
  found_in:
  - Diabetes_Mellitus-deep-research-falcon.md
  findings:
  - statement: The 2022 Canadian Cardiovascular Society (CCS) cardiorenal guideline provided clinical recommendations on sodium-glucose co-transport 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA) use.
    supporting_text: The 2022 Canadian Cardiovascular Society (CCS) cardiorenal guideline provided clinical recommendations on sodium-glucose co-transport 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA) use.
    evidence:
    - reference: DOI:10.1186/s12933-024-02154-w
      reference_title: 'The effectiveness of sodium-glucose co-transporter 2 inhibitors on cardiorenal outcomes: an updated systematic review and meta-analysis'
      supports: SUPPORT
      evidence_source: HUMAN_CLINICAL
      snippet: The 2022 Canadian Cardiovascular Society (CCS) cardiorenal guideline provided clinical recommendations on sodium-glucose co-transport 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA) use.
      explanation: Deep research cited this publication as relevant literature for Diabetes Mellitus.
- reference: DOI:10.17925/ee.2023.19.2.7
  title: 'Teplizumab in Type 1 Diabetes Mellitus: An Updated Review'
  found_in:
  - Diabetes_Mellitus-deep-research-falcon.md
  findings:
  - statement: Type 1 diabetes mellitus (T1DM) is a chronic autoimmune condition characterized by the irreversible destruction of the β cells of the pancreas, which leads to a lifelong dependency on exogenous insulin.
    supporting_text: Type 1 diabetes mellitus (T1DM) is a chronic autoimmune condition characterized by the irreversible destruction of the β cells of the pancreas, which leads to a lifelong dependency on exogenous insulin.
    evidence:
    - reference: DOI:10.17925/ee.2023.19.2.7
      reference_title: 'Teplizumab in Type 1 Diabetes Mellitus: An Updated Review'
      supports: SUPPORT
      evidence_source: HUMAN_CLINICAL
      snippet: Type 1 diabetes mellitus (T1DM) is a chronic autoimmune condition characterized by the irreversible destruction of the β cells of the pancreas, which leads to a lifelong dependency on exogenous insulin.
      explanation: Deep research cited this publication as relevant literature for Diabetes Mellitus.
- reference: DOI:10.2337/dc22-1024
  title: 'Combination of Multiple Low-Risk Lifestyle Behaviors and Incident Type 2 Diabetes: A Systematic Review and Dose-Response Meta-analysis of Prospective Cohort Studies'
  found_in:
  - Diabetes_Mellitus-deep-research-falcon.md
  findings:
  - statement: Combined low-risk lifestyle behaviors (LRLBs) have been associated with a reduction in type 2 diabetes risk.
    supporting_text: Combined low-risk lifestyle behaviors (LRLBs) have been associated with a reduction in type 2 diabetes risk.
    evidence:
    - reference: DOI:10.2337/dc22-1024
      reference_title: 'Combination of Multiple Low-Risk Lifestyle Behaviors and Incident Type 2 Diabetes: A Systematic Review and Dose-Response Meta-analysis of Prospective Cohort Studies'
      supports: SUPPORT
      evidence_source: HUMAN_CLINICAL
      snippet: Combined low-risk lifestyle behaviors (LRLBs) have been associated with a reduction in type 2 diabetes risk.
      explanation: Deep research cited this publication as relevant literature for Diabetes Mellitus.
- reference: DOI:10.26719/2025.31.7.426
  title: A cost of illness study of the economic burden of diabetes in the Eastern Mediterranean Region
  found_in:
  - Diabetes_Mellitus-deep-research-falcon.md
  findings:
  - statement: Diabetes poses an increasing public health and economic challenge in the Eastern Mediterranean Region (EMR), yet its full financial impact across the region remains poorly quantified.
    supporting_text: Diabetes poses an increasing public health and economic challenge in the Eastern Mediterranean Region (EMR), yet its full financial impact across the region remains poorly quantified.
    evidence:
    - reference: DOI:10.26719/2025.31.7.426
      reference_title: A cost of illness study of the economic burden of diabetes in the Eastern Mediterranean Region
      supports: SUPPORT
      evidence_source: HUMAN_CLINICAL
      snippet: Diabetes poses an increasing public health and economic challenge in the Eastern Mediterranean Region (EMR), yet its full financial impact across the region remains poorly quantified.
      explanation: Deep research cited this publication as relevant literature for Diabetes Mellitus.

📚

References & Deep Research

References

DOI:10.1186/s12916-023-02851-5

Dose–response relationship between device-measured physical activity and incident type 2 diabetes: findings from the UK Biobank prospective cohort study

1 finding

Most studies investigating the association between physical activity (PA) and the risk of type 2 diabetes are derived from self-reported questionnaires, with limited evidence using device-based measurements.

"Most studies investigating the association between physical activity (PA) and the risk of type 2 diabetes are derived from self-reported questionnaires, with limited evidence using device-based measurements."

Show evidence (1 reference)

DOI:10.1186/s12916-023-02851-5 SUPPORT Human Clinical

Deep research cited this publication as relevant literature for Diabetes Mellitus.

DOI:10.1186/s12933-024-02154-w

The effectiveness of sodium-glucose co-transporter 2 inhibitors on cardiorenal outcomes: an updated systematic review and meta-analysis

1 finding

The 2022 Canadian Cardiovascular Society (CCS) cardiorenal guideline provided clinical recommendations on sodium-glucose co-transport 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA) use.

"The 2022 Canadian Cardiovascular Society (CCS) cardiorenal guideline provided clinical recommendations on sodium-glucose co-transport 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA) use."

Show evidence (1 reference)

DOI:10.1186/s12933-024-02154-w SUPPORT Human Clinical

Deep research cited this publication as relevant literature for Diabetes Mellitus.

DOI:10.17925/ee.2023.19.2.7

Teplizumab in Type 1 Diabetes Mellitus: An Updated Review

1 finding

Type 1 diabetes mellitus (T1DM) is a chronic autoimmune condition characterized by the irreversible destruction of the β cells of the pancreas, which leads to a lifelong dependency on exogenous insulin.

"Type 1 diabetes mellitus (T1DM) is a chronic autoimmune condition characterized by the irreversible destruction of the β cells of the pancreas, which leads to a lifelong dependency on exogenous insulin."

Show evidence (1 reference)

DOI:10.17925/ee.2023.19.2.7 SUPPORT Human Clinical

Deep research cited this publication as relevant literature for Diabetes Mellitus.

DOI:10.2337/dc22-1024

Combination of Multiple Low-Risk Lifestyle Behaviors and Incident Type 2 Diabetes: A Systematic Review and Dose-Response Meta-analysis of Prospective Cohort Studies

1 finding

Combined low-risk lifestyle behaviors (LRLBs) have been associated with a reduction in type 2 diabetes risk.

"Combined low-risk lifestyle behaviors (LRLBs) have been associated with a reduction in type 2 diabetes risk."

Show evidence (1 reference)

DOI:10.2337/dc22-1024 SUPPORT Human Clinical

"Combined low-risk lifestyle behaviors (LRLBs) have been associated with a reduction in type 2 diabetes risk."

Deep research cited this publication as relevant literature for Diabetes Mellitus.

DOI:10.26719/2025.31.7.426

A cost of illness study of the economic burden of diabetes in the Eastern Mediterranean Region

1 finding

Diabetes poses an increasing public health and economic challenge in the Eastern Mediterranean Region (EMR), yet its full financial impact across the region remains poorly quantified.

"Diabetes poses an increasing public health and economic challenge in the Eastern Mediterranean Region (EMR), yet its full financial impact across the region remains poorly quantified."

Show evidence (1 reference)

DOI:10.26719/2025.31.7.426 SUPPORT Human Clinical

"Diabetes poses an increasing public health and economic challenge in the Eastern Mediterranean Region (EMR), yet its full financial impact across the region remains poorly quantified."

Deep research cited this publication as relevant literature for Diabetes Mellitus.

Deep Research

Falcon ▸

Disease Characteristics Research Template

Edison Scientific Literature 31 citations 2026-05-08T12:54:22.727995

Question: You are an expert researcher providing comprehensive, well-cited information.

Provide detailed information focusing on: 1. Key concepts and definitions with current understanding 2. Recent developments and latest research (prioritize 2023-2024 sources) 3. Current applications and real-world implementations 4. Expert opinions and analysis from authoritative sources 5. Relevant statistics and data from recent studies

Format as a comprehensive research report with proper citations. Include URLs and publication dates where available. Always prioritize recent, authoritative sources and provide specific citations for all major claims.

Disease Characteristics Research Template

Target Disease

Disease Name: Diabetes mellitus
MONDO ID: (if available)
Category: Complex

Research Objectives

Please provide a comprehensive research report on Diabetes mellitus covering all of the disease characteristics listed below. This report will be used to populate a disease knowledge base entry. Be thorough and cite primary literature (PMID preferred) for all claims.

For each section, suggested databases/resources are listed. These are the first places you should search for information on each topic.

1. Disease Information

Search first: OMIM, Orphanet, ICD-10/ICD-11, MeSH, PubMed

What is the disease? Provide a concise overview.
What are the key identifiers? (OMIM, Orphanet, ICD-10/ICD-11, MeSH, Mondo)
What are the common synonyms and alternative names?
Is the information derived from individual patients (e.g., EHR) or aggregated disease-level resources?

2. Etiology

Disease Causal Factors: What are the primary causes? (genetic, environmental, infectious, mechanistic)
Risk Factors:

Search first: PubMed, Cochrane Library, UpToDate, clinical guidelines, ClinVar, ClinGen, GWAS Catalog, PheGenI, CTD, CDC, WHO, epidemiological databases
Genetic risk factors (causal variants, susceptibility loci, modifier genes)
Environmental risk factors (toxins, lifestyle, occupational exposures, age, sex, family history)
Protective Factors:

Search first: PubMed, Cochrane Library, clinical trial databases, GWAS Catalog, gnomAD, WHO, CDC, nutrition databases
Genetic protective factors (protective variants, modifier alleles)
Environmental protective factors (diet, lifestyle, exposures that reduce risk)
Gene-Environment Interactions: How do genetic and environmental factors interact to influence disease?

Search first: CTD, PubMed, PheGenI, GxE databases

3. Phenotypes

Search first: HPO (Human Phenotype Ontology), OMIM, Orphanet, PubMed, clinicaltrials.gov, MedDRA, SNOMED CT, DECIPHER, LOINC

For each phenotype, provide: - Phenotype type: symptoms, clinical signs, physical manifestations, behavioral changes, or laboratory abnormalities

For symptoms/signs: HPO, OMIM, Orphanet, PubMed For behavioral changes: HPO, DSM, RDoC (Research Domain Criteria), PubMed For laboratory abnormalities: LOINC, SNOMED CT, LabTests Online, PubMed - Phenotype characteristics: Search first: OMIM, Orphanet, HPO, PubMed - Age of symptom onset (neonatal, childhood, adult-onset, late-onset) - Symptom severity (mild, moderate, severe, variable) - Symptom progression (stable, progressive, episodic, fluctuating) - Frequency among affected individuals (percentage or qualitative) - Quality of life impact: Effects on daily functioning and well-being (per-phenotype when possible) Search first: EQ-5D database, SF-36, WHO QOL databases, PubMed - Suggest HPO (Human Phenotype Ontology) terms for each phenotype

4. Genetic/Molecular Information

Causal Genes: Gene mutations or chromosomal abnormalities responsible for disease (gene symbols, OMIM IDs)

Search first: OMIM, ClinVar, HGMD, Ensembl, NCBI Gene
Pathogenic Variants:
Affected genes (gene symbols, HGNC IDs) > Search first: OMIM, NCBI Gene, Ensembl, HGNC, UniProt, GeneCards
Variant classification (pathogenic, likely pathogenic, VUS per ACMG/AMP guidelines) > Search first: ClinVar, ClinGen, ACMG/AMP guidelines, VarSome
Variant type/class (missense, frameshift, nonsense, splice-site, structural)
Allele frequency in population databases > Search first: gnomAD, 1000 Genomes, ExAC, TOPMed, dbSNP
Somatic vs germline origin > Search first: COSMIC (somatic), ClinVar, ICGC, TCGA
Functional consequences (loss of function, gain of function, dominant negative)
Modifier Genes: Genes that modify disease severity or expression
Epigenetic Information: DNA methylation, histone modifications, chromatin changes affecting disease

Search first: ENCODE, Roadmap Epigenomics, MethBase, DiseaseMeth
Chromosomal Abnormalities: Large-scale genetic changes (aneuploidy, translocations, inversions)

Search first: DECIPHER, ClinVar, ECARUCA, UCSC Genome Browser

5. Environmental Information

Environmental Factors: Non-genetic contributing factors (toxins, radiation, pollution, occupational exposure)

Search first: CTD (Comparative Toxicogenomics Database), TOXNET, PubMed, EPA databases
Lifestyle Factors: Behavioral factors (smoking, diet, exercise, alcohol consumption)

Search first: CDC databases, WHO, PubMed, NHANES
Infectious Agents: If applicable, pathogens causing or triggering disease (bacteria, viruses, fungi, parasites)

Search first: NCBI Taxonomy, ViPR, BV-BRC, MicrobeDB, GIDEON

6. Mechanism / Pathophysiology

Molecular Pathways: Specific signaling cascades or biochemical pathways involved (Wnt, MAPK, mTOR, PI3K-AKT, etc.)

Search first: KEGG, Reactome, WikiPathways, PathBank, BioCyc
Cellular Processes: Cell-level mechanisms (apoptosis, autophagy, cell cycle dysregulation, inflammation, etc.)

Search first: Gene Ontology (GO), Reactome, KEGG, PubMed
Protein Dysfunction: How protein structure or function is altered (misfolding, aggregation, loss of function, gain of function)

Search first: UniProt, PDB (Protein Data Bank), InterPro, Pfam, AlphaFold
Metabolic Changes: Alterations in metabolic processes (energy metabolism, lipid metabolism, amino acid metabolism)

Search first: KEGG, BioCyc, HMDB (Human Metabolome Database), BRENDA
Immune System Involvement: Role of immune response (autoimmunity, immunodeficiency, chronic inflammation)

Search first: ImmPort, Immunome Database, IEDB, Gene Ontology
Tissue Damage Mechanisms: How tissues/ are injured (oxidative stress, ischemia, fibrosis, necrosis)

Search first: PubMed, Gene Ontology, Reactome
Biochemical Abnormalities: Specific molecular defects (enzyme deficiencies, receptor dysfunction, ion channel defects)

Search first: BRENDA, UniProt, KEGG, OMIM, PubMed
Epigenetic Changes: DNA methylation, histone modifications affecting gene expression in disease

Search first: ENCODE, Roadmap Epigenomics, MethBase, DiseaseMeth
Molecular Profiling (if available):
Transcriptomics/gene expression changes > Search first: GEO (Gene Expression Omnibus), ArrayExpress, GTEx, Human Cell Atlas, SRA
Proteomics findings > Search first: PRIDE, ProteomeXchange, Human Protein Atlas, STRING, BioGRID
Metabolomics signatures > Search first: MetaboLights, Metabolomics Workbench, HMDB, METLIN
Lipidomics alterations > Search first: LIPID MAPS, SwissLipids, LipidHome, Metabolomics Workbench
Genomic structural features > Search first: UCSC Genome Browser, Ensembl, NCBI, dbVar, DGV
Advanced Technologies (if applicable):
Single-cell analysis findings (cell-type specific mechanisms, cellular heterogeneity) > Search first: Human Cell Atlas, Single Cell Portal, GEO, CELLxGENE
Spatial transcriptomics findings > Search first: GEO, Spatial Research, Vizgen, 10x Genomics data
Multi-omics integration results > Search first: TCGA, ICGC, cBioPortal, LinkedOmics, PubMed
Functional genomics screens (CRISPR, RNAi) > Search first: DepMap, GenomeRNAi, PubMed, BioGRID ORCS

For each mechanism, describe: - The causal chain from initial trigger to clinical manifestation - Which mechanisms are upstream vs downstream - What cell types and biological processes are involved - Suggest GO terms for biological processes and CL terms for cell types

7. Anatomical Structures Affected

Organ Level:
Primary organs directly affected
Secondary organ involvement (complications, secondary effects)
Body systems involved (cardiovascular, nervous, digestive, respiratory, endocrine, etc.)

Search first: Uberon, FMA (Foundational Model of Anatomy), OMIM, HPO, ICD-11, MeSH, SNOMED CT
Tissue and Cell Level:
Specific tissue types affected (epithelial, connective, muscle, nervous)
Specific cell populations targeted (with Cell Ontology terms)

Search first: Uberon, Human Protein Atlas, Cell Ontology, Human Cell Atlas, CellMarker, PanglaoDB
Subcellular Level:
Cellular compartments involved (mitochondria, nucleus, ER, lysosomes) (with GO Cellular Component terms)

Search first: Gene Ontology (Cellular Component), UniProt, Human Protein Atlas
Localization:
Specific anatomical sites (with UBERON terms) > Search first: FMA, Uberon, NeuroNames (for brain), SNOMED CT
Lateralization (unilateral, bilateral, asymmetric) > Search first: HPO, clinical literature, imaging databases

8. Temporal Development

Onset:
Typical age of onset (congenital, pediatric, adult, geriatric)
Onset pattern (acute, subacute, chronic, insidious)

Search first: OMIM, Orphanet, HPO, PubMed
Progression:
Disease stages (early, intermediate, advanced, end-stage) > Search first: Cancer Staging Manual (AJCC), WHO classifications, PubMed
Progression rate (rapid, slow, variable)
Disease course pattern (episodic, relapsing-remitting, progressive, stable)
Disease duration (self-limited, chronic lifelong)

Search first: Disease registries, longitudinal cohort databases, natural history studies, PubMed, Orphanet, OMIM
Patterns:
Remission patterns (spontaneous, treatment-induced) > Search first: Clinical trial databases, disease registries, PubMed
Critical periods (time windows of vulnerability or opportunity for intervention) > Search first: PubMed, developmental biology databases, clinical guidelines

9. Inheritance and Population

Epidemiology:
Prevalence (cases per 100,000 at given time)
Incidence (new cases per 100,000 per year)

Search first: Orphanet, CDC, WHO, GBD (Global Burden of Disease), national registries, SEER, disease registries
For Genetic Etiology:
Inheritance pattern (AD, AR, X-linked, mitochondrial, multifactorial, polygenic) > Search first: OMIM, Orphanet, ClinVar, GTR (Genetic Testing Registry)
Penetrance (complete, incomplete, age-dependent) > Search first: ClinVar, OMIM, PubMed, ClinGen
Expressivity (variable, consistent) > Search first: OMIM, ClinVar, PubMed
Genetic anticipation (increasing severity in successive generations) > Search first: OMIM, PubMed (especially for repeat expansion disorders)
Germline mosaicism > Search first: ClinVar, OMIM, genetic counseling literature, PubMed
Founder effects (population-specific mutations) > Search first: gnomAD, population genetics databases, PubMed
Consanguinity role > Search first: OMIM, population studies, genetic counseling resources
Carrier frequency > Search first: gnomAD, carrier screening databases, GeneReviews, GTR
Population Demographics:
Affected populations (ethnic or demographic groups with higher prevalence) > Search first: gnomAD, 1000 Genomes, PAGE Study, PubMed, population registries
Geographic distribution (endemic areas, regional variation) > Search first: WHO, CDC, GBD, Orphanet, geographic epidemiology databases
Geographic distribution of specific variants
Sex ratio (male:female) > Search first: Disease registries, OMIM, PubMed, epidemiological databases
Age distribution of affected individuals > Search first: CDC, disease registries, SEER, Orphanet

10. Diagnostics

Clinical Tests:
Laboratory tests (blood, urine, tissue chemistry, specific enzyme assays) > Search first: LOINC, LabTests Online, PubMed
Biomarkers (proteins, metabolites, genetic markers, circulating biomarkers) > Search first: FDA Biomarker List, BEST (Biomarkers, EndpointS, and other Tools), PubMed
Imaging studies (X-ray, CT, MRI, PET, ultrasound) > Search first: RadLex, DICOM, Radiopaedia, imaging databases
Functional tests (pulmonary function, cardiac stress tests) > Search first: LOINC, clinical guidelines, PubMed
Electrophysiology (EEG, EMG, ECG, nerve conduction studies) > Search first: LOINC, clinical neurophysiology databases, PubMed
Biopsy findings (histopathology, immunohistochemistry) > Search first: SNOMED CT, College of American Pathologists resources, PubMed
Pathology findings (microscopic examination) > Search first: SNOMED CT, Digital Pathology databases, PubMed
Genetic Testing:

Search first: GTR (Genetic Testing Registry), GeneReviews, ClinGen
Overview of recommended genetic testing approach
Whole genome sequencing (WGS) utility > Search first: GTR, ClinVar, GEL (Genomics England), gnomAD
Whole exome sequencing (WES) utility > Search first: GTR, ClinVar, OMIM, GeneMatcher
Gene panels (which panels, which genes) > Search first: GTR, ClinVar, laboratory-specific databases
Single gene testing > Search first: GTR, ClinVar, OMIM, GeneReviews
Chromosomal microarray (CMA) > Search first: DECIPHER, ClinVar, dbVar, ECARUCA
Karyotyping > Search first: Chromosome Abnormality Database, ClinVar, cytogenetics resources
FISH > Search first: ClinVar, cytogenetics databases, PubMed
Mitochondrial DNA testing > Search first: MITOMAP, MSeqDR, ClinVar, GTR
Repeat expansion testing > Search first: GTR, ClinVar, repeat expansion databases, PubMed
Omics-Based Diagnostics (if applicable):
RNA sequencing / transcriptomics > Search first: GEO, ArrayExpress, GTEx, RNA-seq databases
Proteomics > Search first: PRIDE, ProteomeXchange, FDA Biomarker database
Metabolomics > Search first: MetaboLights, Metabolomics Workbench, HMDB
Epigenomics > Search first: GEO, ENCODE, Roadmap Epigenomics, MethBase
Liquid biopsy > Search first: COSMIC, ClinVar, liquid biopsy databases, PubMed
Clinical Criteria:
Standardized diagnostic criteria (DSM, ICD, society guidelines) > Search first: DSM-5, ICD-11, clinical society guidelines, UpToDate
Differential diagnosis (other conditions to rule out, with distinguishing features) > Search first: DynaMed, UpToDate, clinical decision support systems
Screening:
Screening methods for asymptomatic individuals (newborn screening, carrier screening, cascade screening) > Search first: ACMG recommendations, CDC newborn screening, GTR

11. Outcome/Prognosis

Survival and Mortality:
Survival rate (5-year, 10-year, overall) > Search first: SEER, cancer registries, disease-specific registries, PubMed
Life expectancy (with and without treatment if applicable) > Search first: Orphanet, disease registries, actuarial databases, PubMed
Mortality rate > Search first: CDC, WHO, GBD, national mortality databases
Disease-specific mortality (deaths directly attributable to disease) > Search first: Disease registries, CDC Wonder, GBD, PubMed
Morbidity and Function:
Morbidity (disease-related disability and health impacts) > Search first: GBD, WHO, disability databases, PubMed
Disability outcomes (long-term functional impairments) > Search first: ICF (International Classification of Functioning), disability registries
Quality of life measures (EQ-5D, SF-36, PROMIS, disease-specific tools) > Search first: EQ-5D database, SF-36, PROMIS, PubMed
Disease Course:
Complications (secondary problems: infections, organ failure, etc.) > Search first: ICD codes, disease registries, clinical databases, PubMed
Recovery potential (likelihood and extent of recovery, with vs without treatment) > Search first: Natural history studies, rehabilitation databases, PubMed
Prediction:
Prognostic factors (age, disease severity, biomarkers, treatment response) > Search first: Prognostic models databases, clinical calculators, PubMed
Prognostic biomarkers (molecular markers predicting disease course) > Search first: FDA Biomarker database, PubMed, cancer prognostic databases

12. Treatment

Pharmacotherapy:
Pharmacological treatments (drug names, drug classes, mechanisms of action) > Search first: DrugBank, RxNorm, ATC classification, DailyMed, FDA databases
Pharmacogenomics (how genetic variants affect drug metabolism, efficacy, toxicity) > Search first: PharmGKB, CPIC (Clinical Pharmacogenetics), FDA Table of PGx Biomarkers
Advanced Therapeutics:
Gene therapy (viral vectors, CRISPR, gene replacement, gene editing) > Search first: ClinicalTrials.gov, FDA gene therapy database, ASGCT resources
Cell therapy (stem cell transplant, CAR-T, cellular therapeutics) > Search first: ClinicalTrials.gov, FDA cell therapy database, FACT standards
RNA-based therapies (ASOs, siRNA, mRNA therapies) > Search first: ClinicalTrials.gov, FDA approvals, PubMed
Targeted therapies (treatments directed at specific molecular targets) > Search first: My Cancer Genome, OncoKB, ClinicalTrials.gov, FDA approvals
Immunotherapies (checkpoint inhibitors, monoclonal antibodies) > Search first: Cancer Immunotherapy Database, FDA approvals, ClinicalTrials.gov
Surgical and Interventional:
Surgical interventions (types of surgery, timing, outcomes) > Search first: CPT codes, surgical registries, clinical guidelines, PubMed
Supportive and Rehabilitative:
Supportive care (symptom management, pain control, nutrition) > Search first: Clinical guidelines, Cochrane Library, PubMed
Rehabilitation (physical therapy, occupational therapy, speech therapy) > Search first: Rehabilitation medicine databases, clinical guidelines, PubMed
Experimental:
Experimental treatments in clinical trials (with NCT identifiers if available) > Search first: ClinicalTrials.gov, EU Clinical Trials Register, WHO ICTRP
Treatment Outcomes:
Treatment response rates > Search first: Clinical trial databases, FDA reviews, systematic reviews, PubMed
Side effects and adverse events > Search first: FDA Adverse Event Reporting System (FAERS), MedWatch, PubMed
Treatment Strategy:
Treatment algorithms (clinical pathways, decision trees) > Search first: Clinical practice guidelines, NCCN Guidelines, UpToDate
Combination therapies > Search first: ClinicalTrials.gov, treatment guidelines, PubMed
Personalized medicine approaches (genotype-guided treatment) > Search first: My Cancer Genome, CIViC, PharmGKB, precision medicine databases

For each treatment, suggest MAXO (Medical Action Ontology) terms where applicable.

13. Prevention

Prevention Levels:
Primary prevention (preventing disease occurrence: vaccination, risk factor modification) > Search first: CDC, WHO, USPSTF recommendations, Cochrane Library
Secondary prevention (early detection and treatment: screening programs, early intervention) > Search first: USPSTF, CDC screening guidelines, WHO
Tertiary prevention (preventing complications in those with disease) > Search first: Clinical guidelines, disease management protocols, PubMed
Immunization: Vaccine strategies (if applicable)

Search first: CDC vaccine schedules, WHO immunization, FDA vaccine database
Screening and Early Detection:
Screening programs (population-based: newborn screening, cancer screening) > Search first: CDC screening programs, USPSTF, cancer screening databases
Genetic screening (carrier screening, preimplantation genetic diagnosis, prenatal testing) > Search first: ACMG recommendations, ACOG guidelines, GTR
Risk stratification (identifying high-risk individuals for targeted prevention) > Search first: Risk prediction models, clinical calculators, PubMed
Behavioral Interventions: Lifestyle modifications to reduce risk

Search first: CDC, WHO, behavioral intervention databases, Cochrane Library
Counseling: Genetic counseling (risk assessment, family planning guidance)

Search first: NSGC resources, ACMG guidelines, GeneReviews
Public Health:
Public health interventions (sanitation, vector control, health education) > Search first: CDC, WHO, public health databases, PubMed
Environmental interventions (reducing environmental risk factors) > Search first: EPA databases, WHO environmental health, PubMed
Prophylaxis: Preventive medications or procedures

Search first: Clinical guidelines, FDA approvals, PubMed

14. Other Species / Natural Disease

Taxonomy: Species affected (with NCBI Taxon identifiers)

Search first: NCBI Taxonomy
Breed: Specific breeds affected (with VBO identifiers if applicable)

Search first: VBO (Vertebrate Breed Ontology)
Gene: Orthologous genes in other species (with NCBI Gene IDs)

Search first: NCBI Gene
Natural Disease:
Naturally occurring disease in other species (companion animals, wildlife) > Search first: OMIA (Online Mendelian Inheritance in Animals), VetCompass, PubMed
Veterinary relevance and importance in animal health > Search first: OMIA, veterinary databases, PubMed
Comparative Biology:
Comparative pathology (similarities and differences across species) > Search first: OMIA, comparative pathology databases, PubMed
Evolutionary conservation of disease mechanisms > Search first: HomoloGene, OrthoMCL, Alliance of Genome Resources
Transmission (if applicable):
Zoonotic potential > Search first: CDC zoonotic diseases, WHO zoonoses, GIDEON
Cross-species susceptibility > Search first: NCBI Taxonomy, veterinary databases, PubMed

15. Model Organisms

Model Types:
Model organism type (mammalian, invertebrate, cellular, in vitro) > Search first: Alliance of Genome Resources, model organism databases
Specific model systems (mouse, rat, zebrafish, Drosophila, C. elegans, yeast, cell lines, organoids, iPSCs) > Search first: MGI, RGD, ZFIN, FlyBase, WormBase, SGD, ATCC, Cellosaurus
Induced models (drug treatment, surgical intervention, environmental manipulation) > Search first: MGI, model organism databases, PubMed
Genetic Models:
Types available (knockout, knock-in, transgenic, conditional, humanized) > Search first: MGI, IMPC, KOMP, EuMMCR, IMSR
Model Characteristics:
Phenotype recapitulation (how well model reproduces human disease features) > Search first: Model organism databases, comparative studies, PubMed
Model limitations (aspects of human disease not captured) > Search first: Model organism databases, PubMed, review articles
Applications:
Research applications (what aspects of disease can be studied) > Search first: Model organism databases, PubMed
Resources:
Model databases > Search first: MGI, RGD, ZFIN, FlyBase, WormBase, IMSR, EMMA, MMRRC

Citation Requirements

Cite primary literature (PMID preferred) for all mechanistic and clinical claims
Prioritize recent reviews and landmark papers
Include direct quotes from abstracts where possible to support key statements
Distinguish evidence source types: human clinical, model organism, in vitro, computational

Output Format

Structure your response as a comprehensive narrative organized by the sections above. For each section, provide: - Factual content with specific details (numbers, percentages, gene names, variant nomenclature) - Ontology term suggestions (HPO, GO, CL, UBERON, CHEBI, MAXO, MONDO) where applicable - Evidence citations with PMIDs - Direct quotes from abstracts to support key claims - Clear indication when information is not available or not applicable for this disease

This report will be used to populate a disease knowledge base entry with: - Pathophysiology descriptions with causal chains - Gene/protein annotations (HGNC, GO terms) - Phenotype associations (HP terms) with frequencies - Cell type involvement (CL terms) - Anatomical locations (UBERON terms) - Chemical entities (CHEBI terms) - Treatment annotations (MAXO terms) - Evidence items with PMIDs and exact abstract quotes - Epidemiology, prognosis, diagnostic, and prevention information - Animal model descriptions with phenotype recapitulation details

Comprehensive Disease Characteristics Report: Diabetes Mellitus (Complex disease)

Executive summary (current understanding)

Diabetes mellitus (DM) is a group of chronic metabolic disorders characterized by persistent hyperglycemia due to defects in insulin secretion, insulin action, or both, and it is conventionally classified into type 1 diabetes (T1D), type 2 diabetes (T2D), gestational diabetes mellitus (GDM), and other specific types (e.g., monogenic diabetes, exocrine pancreatic disease, drug/chemical-induced diabetes). (yameny2024diabetesmellitusoverview pages 1-2, care20242.diagnosisand pages 4-5)

In modern clinical practice, diabetes is increasingly viewed as heterogeneous with overlapping phenotypes and endotypes; misclassification at diagnosis is common (e.g., adults with new T1D misdiagnosed as T2D). (care20242.diagnosisand pages 4-5, care20242.diagnosisand pages 5-6)

Recent (2024) evidence syntheses show that two glucose-lowering drug classes—SGLT2 inhibitors and GLP-1 receptor agonists—provide clinically meaningful reductions in cardiovascular and kidney outcomes beyond glycemic control in high-risk adults, with partially distinct benefits (e.g., stronger heart failure benefit for SGLT2 inhibitors; stroke benefit for GLP-1 RAs). (diabetology2024theeffectivenessof pages 1-2, diabetology2024theeffectivenessof pages 5-7)

Evidence and citation note

This report is constrained to sources retrievable via the provided tools in this run. Several ontology/identifier items (e.g., MONDO, MeSH tree, ICD-11 details, OMIM disease records) were not directly retrievable in the current tool outputs, so those elements are flagged as “not retrieved in tool-accessed sources” rather than inferred.

1. Disease information

1.1 What is the disease?

Diabetes mellitus is defined as persistent hyperglycemia caused by impaired insulin secretion and/or impaired insulin action, with chronic microvascular and macrovascular complications risk across types. (yameny2024diabetesmellitusoverview pages 1-2, care20242.diagnosisand pages 5-6)

1.2 Key identifiers

MONDO ID: not retrieved in the tool-accessed sources for this run.
ICD-10/ICD-11, MeSH, OMIM/Orphanet identifiers: not retrieved in the tool-accessed sources for this run.

1.3 Common synonyms / alternative names

Common clinical labels embedded in ADA classification include: type 1 diabetes (including latent autoimmune diabetes in adults, LADA), type 2 diabetes, gestational diabetes mellitus, and “specific types due to other causes” (including monogenic diabetes syndromes). (care20242.diagnosisand pages 4-5, care20242.diagnosisand pages 5-6)

1.4 Data provenance

Information used here is derived from aggregated, disease-level resources (e.g., ADA Standards of Care, meta-analyses, population surveys, and clinical trials), not individual EHR records. (care20242.diagnosisanda pages 2-2, diabetology2024theeffectivenessof pages 1-2)

2. Etiology

2.1 Disease causal factors (high level)

T1D: autoimmune destruction of pancreatic β cells leading to absolute insulin deficiency, mediated largely by autoreactive T cells (including cytotoxic CD8+ T cells) and characterized by islet autoantibodies appearing before clinical onset. (thakkar2023teplizumabintype pages 1-2)
T2D: progressive loss of adequate β-cell insulin secretion, frequently on a background of insulin resistance; the “paths to β-cell demise and dysfunction” are heterogeneous. (care20242.diagnosisand pages 5-6, care20242.diagnosisand pages 4-5)

Direct abstract quote supporting T1D autoimmune etiology: the teplizumab review describes T1D as “a chronic autoimmune condition characterized by the irreversible destruction of the β cells of the pancreas.” (thakkar2023teplizumabintype pages 1-2)

2.2 Risk factors

Genetic risk factors (examples)

T1D familial aggregation and genetic susceptibility are strongly influenced by the HLA region (e.g., DR3-DQ2, DR4-DQ8), and identical-twin concordance is substantial. (thakkar2023teplizumabintype pages 1-2)
A major diagnostic/etiologic signal for T1D risk is persistent multiple islet autoantibodies; ADA notes that “persistent presence of two or more islet autoantibodies is a near-certain predictor of clinical diabetes” in at-risk cohorts. (care20242.diagnosisand pages 5-6)

Environmental/lifestyle risk factors (T2D)

Large prospective evidence supports lifestyle exposures as major determinants of incident T2D: - Physical activity (device-measured): In UK Biobank (n=40,431; 591 incident T2D cases over median 6.3 years), compared with <150 min/week of moderate PA, 150–300, 300–600, and >600 min/week were associated with 49%, 62%, and 71% lower T2D risk, respectively. (boonpor2023dose–responserelationshipbetween pages 1-2) - Combined lifestyle pattern: A 2023 Diabetes Care systematic review/meta-analysis pooling 30 cohort comparisons (n=1,693,753; 75,669 incident cases) found highest vs lowest adherence to combined low-risk lifestyle behaviors (healthy weight, healthy diet, regular exercise, non-smoking, light alcohol) associated with 80% lower T2D risk (RR 0.20; 95% CI 0.17–0.23). (khan2023combinationofmultiple pages 1-3)

2.3 Protective factors

Multiple combined low-risk lifestyle behaviors show strong protective association, with a graded dose–response: each additional low-risk behavior associated with ~33% lower risk (RR 0.67; 95% CI 0.64–0.70), and maximum adherence estimated RR 0.15 (95% CI 0.12–0.18). (khan2023combinationofmultiple pages 8-10)
Moderate-to-high physical activity shows graded protection and only partially mediated by BMI (12–20% mediation), suggesting benefits beyond weight reduction alone. (boonpor2023dose–responserelationshipbetween pages 1-2)

2.4 Gene–environment interactions

Specific gene–environment interaction mechanisms were not directly retrievable in the tool-accessed sources for this run. However, the ADA Standards emphasize that both genetic and environmental factors contribute to progressive β-cell loss/dysfunction across diabetes types. (care20242.diagnosisand pages 5-6)

3. Phenotypes

3.1 Core phenotypes (symptoms/signs/lab abnormalities)

Hyperglycemia (laboratory abnormality) defining phenotype across DM types. (care20242.diagnosisanda pages 2-2, care20242.diagnosisand pages 2-2)
Classic hyperglycemia symptoms (clinical phenotype) support diagnosis when random plasma glucose ≥200 mg/dL occurs with symptoms or hyperglycemic crisis. (care20242.diagnosisanda pages 2-2, care20242.diagnosisand pages 2-2)
Diabetic ketoacidosis (DKA) is a key acute presentation risk, especially in insulinopenic states; ADA highlights DKA prevention benefit from earlier identification/follow-up in at-risk children. (care20242.diagnosisand pages 8-9)

3.2 Chronic complications (high level)

All diabetes forms confer risk for chronic complications once hyperglycemia occurs, with potentially different rates of progression by diabetes subtype. (care20242.diagnosisand pages 5-6)

3.3 Suggested HPO terms (non-exhaustive, for knowledge base population)

Not all HPO terms were validated against HPO in this tool run; below are reasonable mapping suggestions based on standard clinical phenotype structure: - Hyperglycemia: HP:0003074 (suggested) - Polydipsia: HP:0001959 (suggested) - Polyuria: HP:0000103 (suggested) - Diabetic ketoacidosis: HP:0001953 (suggested) - Abnormal glycated hemoglobin: HP:0030663 (suggested)

Frequency/severity estimates for symptoms/complications were not directly extractable from the tool-accessed sources for this run.

4. Genetic / molecular information

4.1 Causal genes

For polygenic T1D/T2D, specific causal genes were not enumerated in the retrievable sources here; HLA risk is emphasized for T1D (thakkar2023teplizumabintype pages 1-2).
For monogenic diabetes, ADA explicitly recognizes “monogenic diabetes syndromes” within the “specific types of diabetes due to other causes” category and provides clinical features that raise suspicion (e.g., family history, A1C <7.5% at diagnosis, syndromic features), but does not list a gene set in the extracted pages. (care20242.diagnosisand pages 4-5, care20242.diagnosisand pages 5-6)

4.2 Pathogenic variants / allele frequency / somatic vs germline

Not retrievable in the tool-accessed sources for this run.

4.3 Epigenetic information

Not retrievable in the tool-accessed sources for this run.

5. Environmental information

5.1 Lifestyle factors

Lifestyle determinants with strong cohort evidence are summarized above (Section 2) with quantitative risk reductions for physical activity and combined lifestyle patterns. (boonpor2023dose–responserelationshipbetween pages 1-2, khan2023combinationofmultiple pages 1-3)

5.2 Infectious agents

ADA notes enteroviruses (e.g., Coxsackievirus B) as associated with T1D and discusses mixed evidence regarding COVID-19 as a trigger/accelerator in susceptible individuals during the early pandemic, with later cohorts showing mixed findings and possible confounding by delayed diagnosis/access. (care20242.diagnosisand pages 8-9)

6. Mechanism / pathophysiology

6.1 Causal chains (simplified)

T1D

Genetic susceptibility and environmental triggers → loss of immune tolerance → appearance of islet autoantibodies → autoreactive T cell infiltration/β-cell killing (CD8+ T cells; impaired regulation by Tregs) → progressive β-cell loss → absolute insulin deficiency → hyperglycemia and risk of acute metabolic decompensation (e.g., DKA). (thakkar2023teplizumabintype pages 1-2, care20242.diagnosisand pages 5-6)

T2D

Obesogenic environment + genetic susceptibility → insulin resistance in metabolic tissues with compensatory hyperinsulinemia → progressive β-cell dysfunction and inadequate insulin secretion → chronic hyperglycemia → vascular and other tissue injury → microvascular and macrovascular complications. (care20242.diagnosisand pages 5-6, yameny2024diabetesmellitusoverview pages 1-2)

6.2 Key pathways/processes (ontology-mappable suggestions)

The specific pathway names (e.g., PI3K-AKT, mTOR) were not explicitly provided in the tool-accessed sources for this run, but standard disease mapping for diabetes commonly includes: - GO biological process suggestions: “regulation of insulin secretion”, “glucose homeostasis”, “inflammatory response”, “T cell activation” (suggested; not directly validated in retrieved text).

6.3 Key cell types (CL suggestions)

Pancreatic β cell (CL:0000169; suggested)
CD8-positive, alpha-beta T cell (CL:0000625; suggested)
Regulatory T cell (CL:0000815; suggested)

Evidence for CD8+ T cell cytotoxicity and Treg involvement in T1D is explicit in retrieved sources. (thakkar2023teplizumabintype pages 1-2)

6.4 Key anatomical sites (UBERON suggestions)

Pancreas (UBERON:0001264; suggested)
Pancreatic islet of Langerhans (UBERON:0000007; suggested)

7. Anatomical structures affected

Primary: endocrine pancreas/islets (β cells) in T1D; systemic metabolic tissues involved in insulin resistance in T2D; multi-organ vascular beds underlying chronic complications across types once hyperglycemia occurs. (care20242.diagnosisand pages 5-6, thakkar2023teplizumabintype pages 1-2)

8. Temporal development

T1D: autoantibody seroconversion can occur early in life; ADA notes peak seroconversion between 9–24 months and that glycemic changes can precede diagnosis by months. (care20242.diagnosisand pages 5-6)
Both T1D and T2D occur across age groups; “traditional paradigms” (T2D only adults, T1D only children) are inaccurate. (care20242.diagnosisand pages 3-4)

9. Inheritance and population

9.1 Epidemiology (recent statistics)

Global: 537 million adults (20–79) living with diabetes in 2021 (10.5%), projected to 643 million by 2030 and 783 million by 2045 (IDF-based figures cited in multiple 2024–2025 sources). (elmusharaf2025acostof pages 1-2, yameny2024diabetesmellitusoverview pages 1-2)
Diabetes-attributable mortality and expenditures (global, 2021): 6.7 million deaths and US$ 966 billion in health expenditures are cited from IDF/2021-based estimates. (elmusharaf2025acostof pages 1-2)
Regional example (Eastern Mediterranean Region, EMR): >74 million people with diabetes and ~833,000 deaths in 2023, projected to 150 million cases and 2 million deaths by 2050. (elmusharaf2025acostof pages 1-2)
Country example (Korea): adult prevalence (≥30 years) 15.5% in 2021–2022, with only 32.4% achieving HbA1c <6.5% and 15.9% achieving integrated HbA1c/BP/LDL targets. (park2025diabetesfactsheets pages 1-3)

9.2 Genetic architecture

The tool-accessed sources emphasize multifactorial/polygenic inheritance for common forms and highlight HLA contribution for T1D susceptibility. (thakkar2023teplizumabintype pages 1-2, care20242.diagnosisand pages 5-6)

10. Diagnostics

10.1 Guideline diagnostic criteria (ADA Standards of Care)

Diagnostic and prediabetes thresholds are shown in ADA tables (images extracted) and text: - Diabetes: A1C ≥6.5%; FPG ≥126 mg/dL; 2-h OGTT ≥200 mg/dL; random plasma glucose ≥200 mg/dL with classic symptoms or crisis. (care20242.diagnosisanda pages 2-2, care20242.diagnosisand pages 2-2, care20242.diagnosisand media a404318a) - Prediabetes: A1C 5.7–6.4%; FPG 100–125 mg/dL; 2-h OGTT 140–199 mg/dL. (care20242.diagnosisand pages 2-3, care20242.diagnosisand media aa0d0872) - Confirmation: in absence of unequivocal hyperglycemia, diagnosis requires two abnormal results (repeat or different test). (care20242.diagnosisanda pages 2-2, care20242.diagnosisand pages 3-4) - A1C caveats: use plasma glucose criteria when A1C–glycemia relationship is altered (e.g., pregnancy, hemoglobin variants, anemia/altered RBC turnover, HIV, kidney failure/dialysis). (care20242.diagnosisand pages 2-3, care20242.diagnosisand pages 3-4)

10.2 Biomarkers for classification

Islet autoantibodies (e.g., GAD, IA-2, ZnT8) and C-peptide are used to support classification in adults with suspected T1D and to consider monogenic diabetes when clinical features suggest. (care20242.diagnosisand pages 4-5, care20242.diagnosisand pages 5-6)

11. Outcome / prognosis

11.1 Complications risk and outcomes

Once hyperglycemia occurs, all diabetes types confer risk of chronic complications; contemporary management emphasizes preventing acute complications (e.g., DKA) and reducing cardiorenal morbidity using therapies with outcomes evidence. (care20242.diagnosisand pages 5-6, diabetology2024theeffectivenessof pages 1-2)

11.2 Recent quantitative outcomes evidence (cardiorenal protection)

A 2024 updated meta-analysis pooling 151,023 adults (90,943 in SGLT2 inhibitor trials; 60,080 in GLP-1RA trials) reports reductions vs standard care: CV mortality (SGLT2i 14%; GLP-1RA 13%), all-cause mortality (12% both), MACE (SGLT2i 11%; GLP-1RA 14%), HF hospitalization (SGLT2i 30%; GLP-1RA 9%), and kidney composite outcomes (SGLT2i 32%; GLP-1RA 22%). (diabetology2024theeffectivenessof pages 1-2)

12. Treatment

12.1 Standard of care categories (high level)

Management includes lifestyle interventions, pharmacotherapy (including insulin, GLP-1 receptor agonists, SGLT2 inhibitors), and in selected cases metabolic surgery or advanced biologic/cell therapies. (yameny2024diabetesmellitusoverview pages 1-2, care20242.diagnosisand pages 5-6)

12.2 Cardiorenal outcome–directed pharmacotherapy (2023–2024 emphasis)

SGLT2 inhibitors and GLP-1 RAs (outcomes evidence): the 2024 meta-analysis indicates class-level reductions across major outcomes, with differential signals (SGLT2i stronger HF hospitalization benefit; GLP-1RA stroke benefit). (diabetology2024theeffectivenessof pages 1-2, diabetology2024theeffectivenessof pages 5-7)
GLP-1RA CV/kidney outcomes meta-analysis (2024): A June 2024 meta-analysis of 13 placebo-controlled GLP-1RA CVOTs (83,258 patients) reported MACE reduction (OR 0.86, 95% CI 0.80–0.94) and kidney composite reduction (OR 0.76, 95% CI 0.67–0.85), among other endpoints. (rivera2024cardiovascularandrenal pages 1-2)
Semaglutide kidney outcomes in SELECT (2024, real-world adjacent implications): In SELECT (overweight/obesity with established CVD without diabetes), semaglutide lowered a prespecified kidney composite (1.8% vs 2.2%; HR 0.78; 95% CI 0.63–0.96) and improved eGFR slope at 104 weeks. (colhoun2024longtermkidneyoutcomes pages 1-2)

12.3 Advanced therapeutics (latest research; real-world implementation status varies)

Disease-modifying immunotherapy to delay T1D: Teplizumab (anti-CD3) is described as FDA-licensed (Nov 2022) to delay onset of clinical T1D in high-risk individuals ≥8 years (stage 2 T1D), with proposed mechanisms including enhancement of regulatory T-cell activity and promotion of immune tolerance. (thakkar2023teplizumabintype pages 1-2)
β-cell replacement / stem-cell–derived islet therapy (2025, pivotal clinical development): A phase 1–2 study of zimislecel (VX-880; allogeneic stem cell–derived fully differentiated islets) reported detectable C-peptide after infusion in all 14 participants (all had undetectable baseline C-peptide), and among 12 full-dose recipients, 83% were insulin independent at day 365, with HbA1c <7% and >70% time in range; safety signals included neutropenia and two deaths. (reichman2025stemcellderivedfully pages 1-2)

12.4 MAXO term suggestions (non-exhaustive)

Not validated against MAXO in this run; suggested mappings: - Insulin therapy; glucose monitoring; lifestyle intervention; bariatric surgery/metabolic surgery; immunotherapy (anti-CD3); islet cell transplantation/cell therapy.

13. Prevention

13.1 Primary prevention (T2D)

Physical activity: graded dose-response protection against incident T2D in device-measured cohort data. (boonpor2023dose–responserelationshipbetween pages 1-2)
Multi-behavior lifestyle pattern: very large risk reductions associated with combined low-risk lifestyle behaviors in prospective cohorts, with strong dose-response. (khan2023combinationofmultiple pages 1-3, khan2023combinationofmultiple pages 8-10)

13.2 Secondary prevention (screening/early detection)

ADA provides risk-based screening and emphasizes accurate use of diagnostic criteria, confirmatory testing, and appropriate selection of A1C vs plasma glucose tests depending on clinical context. (care20242.diagnosisand pages 2-3, care20242.diagnosisanda pages 2-2)

13.3 Tertiary prevention (prevent complications)

Outcomes-focused therapy with SGLT2 inhibitors and/or GLP-1 RAs for appropriate patients is supported by 2024 evidence synthesis showing reductions in MACE, HF hospitalization, and kidney composites. (diabetology2024theeffectivenessof pages 1-2)

14. Other species / natural disease

Not retrievable in the tool-accessed sources for this run.

15. Model organisms

Not retrievable in the tool-accessed sources for this run.

Consolidated evidence table (diagnosis + key 2024–2025 outcomes)

Domain	Measure/Endpoint	Quantitative result	Population/Context	Source (paper + year)
Diagnosis	Diabetes: A1C	≥6.5% (≥48 mmol/mol)	ADA diagnostic threshold for diabetes in nonpregnant individuals; laboratory NGSP-certified assay recommended	ADA Standards of Care 2024 (care20242.diagnosisanda pages 2-2, care20242.diagnosisand pages 2-2)
Diagnosis	Diabetes: Fasting plasma glucose	≥126 mg/dL (≥7.0 mmol/L)	Fasting defined as no caloric intake for at least 8 h	ADA Standards of Care 2024 (care20242.diagnosisanda pages 2-2, care20242.diagnosisand pages 2-2, care20242.diagnosisand pages 1-2)
Diagnosis	Diabetes: 2-h plasma glucose during 75-g OGTT	≥200 mg/dL (≥11.1 mmol/L)	ADA diagnostic threshold; OGTT more sensitive than A1C in some settings	ADA Standards of Care 2024 (care20242.diagnosisanda pages 2-2, care20242.diagnosisand pages 2-2)
Diagnosis	Diabetes: Random plasma glucose	≥200 mg/dL (≥11.1 mmol/L)	Requires classic hyperglycemia symptoms or hyperglycemic crisis	ADA Standards of Care 2024 (care20242.diagnosisanda pages 2-2, care20242.diagnosisand pages 2-2)
Diagnosis	Prediabetes: A1C	5.7–6.4% (39–47 mmol/mol)	ADA high-risk/prediabetes range	ADA Standards of Care 2024 (care20242.diagnosisand pages 2-3, care20242.diagnosisanda pages 2-3, care20242.diagnosisand pages 10-10)
Diagnosis	Prediabetes: Fasting plasma glucose	100–125 mg/dL (5.6–6.9 mmol/L)	Impaired fasting glucose	ADA Standards of Care 2024 (care20242.diagnosisand pages 2-3, care20242.diagnosisanda pages 2-3, care20242.diagnosisand pages 10-10)
Diagnosis	Prediabetes: 2-h plasma glucose during 75-g OGTT	140–199 mg/dL (7.8–11.0 mmol/L)	Impaired glucose tolerance	ADA Standards of Care 2024 (care20242.diagnosisand pages 2-3, care20242.diagnosisanda pages 2-3, care20242.diagnosisand pages 10-10)
Diagnosis	Confirmation requirement	2 abnormal results from the same or different tests unless unequivocal hyperglycemia is present	Repeat/confirmatory testing recommended when no classic symptoms	ADA Standards of Care 2024 (care20242.diagnosisanda pages 2-2, care20242.diagnosisand pages 2-2, care20242.diagnosisand pages 3-4)
Diagnosis	Key A1C caveats	Use plasma glucose criteria instead when A1C-glycemia relationship is altered	Examples: hemoglobin variants, pregnancy, G6PD deficiency, anemia/altered RBC turnover, erythropoietin use, hemodialysis/kidney failure, HIV, transfusion/hemolysis	ADA Standards of Care 2024 (care20242.diagnosisand pages 2-3, care20242.diagnosisand pages 3-4, care20242.diagnosisanda pages 2-3)
Diagnosis	OGTT pre-test preparation	≥150 g carbohydrate/day for 3 days before test	Helps avoid false-positive postchallenge glucose results	ADA Standards of Care 2024 (care20242.diagnosisand pages 2-3, care20242.diagnosisanda pages 2-3)
Treatment/Outcomes	SGLT2 inhibitors: CV mortality	14% reduction	Pooled RCT meta-analysis across cardiorenal trials; total n=151,023 adults overall (90,943 in SGLT2i trials)	Sherifali et al., 2024 (diabetology2024theeffectivenessof pages 1-2)
Treatment/Outcomes	GLP-1 receptor agonists: CV mortality	13% reduction	Pooled RCT meta-analysis across cardiorenal trials; total n=151,023 adults overall (60,080 in GLP-1RA trials)	Sherifali et al., 2024 (diabetology2024theeffectivenessof pages 1-2)
Treatment/Outcomes	SGLT2 inhibitors: MACE	11% reduction	Pooled RCT meta-analysis	Sherifali et al., 2024 (diabetology2024theeffectivenessof pages 1-2)
Treatment/Outcomes	GLP-1 receptor agonists: MACE	14% reduction	Pooled RCT meta-analysis	Sherifali et al., 2024 (diabetology2024theeffectivenessof pages 1-2)
Treatment/Outcomes	SGLT2 inhibitors: HF hospitalization	30% reduction	Pooled RCT meta-analysis; in T2D subgroup, SGLT2i uniquely reduced HF hospitalization	Sherifali et al., 2024 (diabetology2024theeffectivenessof pages 1-2)
Treatment/Outcomes	GLP-1 receptor agonists: HF hospitalization	9% reduction	Pooled RCT meta-analysis	Sherifali et al., 2024 (diabetology2024theeffectivenessof pages 1-2)
Treatment/Outcomes	SGLT2 inhibitors: Kidney composite	32% reduction overall; 33% reduction in T2D subgroup (HR 0.67, 95% CI 0.59–0.75)	Pooled RCT meta-analysis/update	Sherifali et al., 2024 (diabetology2024theeffectivenessof pages 5-7, diabetology2024theeffectivenessof pages 1-2)
Treatment/Outcomes	GLP-1 receptor agonists: Kidney composite	22% reduction overall; HR 0.78 (95% CI 0.70–0.87) in T2D subgroup	Pooled RCT meta-analysis/update	Sherifali et al., 2024 (diabetology2024theeffectivenessof pages 5-7, diabetology2024theeffectivenessof pages 1-2)
Treatment/Outcomes	GLP-1 receptor agonists: nonfatal stroke	16% reduction; HR 0.84 (95% CI 0.76–0.94)	T2D subgroup meta-analysis; GLP-1RA showed stroke benefit not seen with SGLT2i	Sherifali et al., 2024 (diabetology2024theeffectivenessof pages 5-7)
Treatment/Outcomes	SELECT semaglutide kidney composite	1.8% vs 2.2%; HR 0.78 (95% CI 0.63–0.96), P=0.02	Overweight/obesity with established CVD, without diabetes; semaglutide n=8,803 vs placebo n=8,801	Colhoun et al., 2024 (colhoun2024longtermkidneyoutcomes pages 1-2)
Treatment/Outcomes	SELECT semaglutide eGFR benefit at 104 weeks	+0.75 mL/min/1.73 m² overall; +2.19 mL/min/1.73 m² if baseline eGFR <60	Prespecified kidney analysis in SELECT	Colhoun et al., 2024 (colhoun2024longtermkidneyoutcomes pages 1-2)
Advanced therapy	Zimislecel (VX-880): insulin independence at day 365	10/12 (83%)	Full-dose recipients with type 1 diabetes in phase 1–2 study; all 12/12 also free of severe hypoglycemic events and HbA1c <7%	Reichman et al., 2025 (reichman2025stemcellderivedfully pages 1-2)
Advanced therapy	Zimislecel (VX-880): engraftment/islet function	Detectable C-peptide in all 14 participants after infusion	Type 1 diabetes; C-peptide undetectable at baseline in all 14	Reichman et al., 2025 (reichman2025stemcellderivedfully pages 1-2)

Table: This table compiles ADA diagnostic thresholds for diabetes and prediabetes, including key testing caveats, alongside recent quantitative treatment and outcome findings for SGLT2 inhibitors, GLP-1 receptor agonists, semaglutide in SELECT, and stem-cell derived islet therapy. It is useful as a compact evidence summary for diagnosis and current therapeutic impact.

Key visual evidence (guideline tables)

Images extracted from the ADA Standards include the diagnostic criteria for diabetes and prediabetes (Table 2.1/2.2) and can be used as visual support for threshold values. (care20242.diagnosisand media a404318a, care20242.diagnosisand media aa0d0872)

References (URLs and publication dates available in retrieved sources)

ADA Standards of Care (Diagnosis & Classification): “2. Diagnosis and classification of diabetes: Standards of care in diabetes—2024” (retrieved text excerpts include criteria and caveats; URL indicated in document as https://diabetesjournals.org/care). (care20242.diagnosisanda pages 2-2, care20242.diagnosisand pages 2-3)
Sherifali et al. “The effectiveness of sodium-glucose co-transporter 2 inhibitors on cardiorenal outcomes: an updated systematic review and meta-analysis.” Cardiovascular Diabetology, Feb 2024. https://doi.org/10.1186/s12933-024-02154-w (diabetology2024theeffectivenessof pages 1-2)
Rivera et al. “Cardiovascular and renal outcomes of GLP-1 receptor agonists…” American Journal of Preventive Cardiology, Jun 2024 (online 7 May 2024). https://doi.org/10.1016/j.ajpc.2024.100679 (rivera2024cardiovascularandrenal pages 1-2)
Colhoun et al. “Long-term kidney outcomes of semaglutide in obesity and cardiovascular disease in the SELECT trial.” Nature Medicine, May 2024 (online 25 May 2024). https://doi.org/10.1038/s41591-024-03015-5 (colhoun2024longtermkidneyoutcomes pages 1-2)
Thakkar et al. “Teplizumab in Type 1 Diabetes Mellitus: An Updated Review.” touchREVIEWS in Endocrinology, Oct 2023. https://doi.org/10.17925/ee.2023.19.2.7 (thakkar2023teplizumabintype pages 1-2)
Reichman et al. “Stem Cell-Derived, Fully Differentiated Islets for Type 1 Diabetes.” NEJM, Jun 2025. https://doi.org/10.1056/NEJMoa2506549 (reichman2025stemcellderivedfully pages 1-2)
Khan et al. “Combination of Multiple Low-Risk Lifestyle Behaviors and Incident Type 2 Diabetes.” Diabetes Care, Feb 2023. https://doi.org/10.2337/dc22-1024 (khan2023combinationofmultiple pages 1-3)
Boonpor et al. “Dose–response relationship between device-measured physical activity and incident type 2 diabetes.” BMC Medicine, May 2023. https://doi.org/10.1186/s12916-023-02851-5 (boonpor2023dose–responserelationshipbetween pages 1-2)
Elmusharaf et al. “A cost of illness study of the economic burden of diabetes in the Eastern Mediterranean Region.” Eastern Mediterranean Health Journal, Aug 2025 (contains IDF-based global 2021 prevalence/mortality/expenditure and EMR 2023 estimates). https://doi.org/10.26719/2025.31.7.426 (elmusharaf2025acostof pages 1-2)
Park et al. “Diabetes Fact Sheets in Korea 2024.” Diabetes & Metabolism Journal, Jan 2025. https://doi.org/10.4093/dmj.2024.0818 (park2025diabetesfactsheets pages 1-3)

References

(yameny2024diabetesmellitusoverview pages 1-2): Ahmed Abdelhalim Yameny. Diabetes mellitus overview 2024. Journal of Bioscience and Applied Research, 10:641-645, Sep 2024. URL: https://doi.org/10.21608/jbaar.2024.382794, doi:10.21608/jbaar.2024.382794. This article has 203 citations.
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(care20242.diagnosisand pages 5-6): D Care. 2. diagnosis and classification of diabetes: standards of care in diabetes—2024. Unknown journal, 2024.
(diabetology2024theeffectivenessof pages 1-2): Cardiovascular Diabetology, D. Sherifali, Muhammad Usman Ali, G. B. J. Mancini, D. Fitzpatrick-Lewis, Kim A. Connelly, E. O’Meara, and Shelley Zieroth. The effectiveness of sodium-glucose co-transporter 2 inhibitors on cardiorenal outcomes: an updated systematic review and meta-analysis. Cardiovascular Diabetology, Feb 2024. URL: https://doi.org/10.1186/s12933-024-02154-w, doi:10.1186/s12933-024-02154-w. This article has 21 citations and is from a peer-reviewed journal.
(diabetology2024theeffectivenessof pages 5-7): Cardiovascular Diabetology, D. Sherifali, Muhammad Usman Ali, G. B. J. Mancini, D. Fitzpatrick-Lewis, Kim A. Connelly, E. O’Meara, and Shelley Zieroth. The effectiveness of sodium-glucose co-transporter 2 inhibitors on cardiorenal outcomes: an updated systematic review and meta-analysis. Cardiovascular Diabetology, Feb 2024. URL: https://doi.org/10.1186/s12933-024-02154-w, doi:10.1186/s12933-024-02154-w. This article has 21 citations and is from a peer-reviewed journal.
(care20242.diagnosisanda pages 2-2): D Care. 2. diagnosis and classification of diabetes: standards of care in diabetes—2024. Unknown journal, 2024.
(thakkar2023teplizumabintype pages 1-2): Simran Thakkar, Aditi Chopra, Lakshmi Nagendra, Sanjay Kalra, and Saptarshi Bhattacharya. Teplizumab in type 1 diabetes mellitus: an updated review. touchREVIEWS in Endocrinology, 19:22-30, Oct 2023. URL: https://doi.org/10.17925/ee.2023.19.2.7, doi:10.17925/ee.2023.19.2.7. This article has 40 citations.
(boonpor2023dose–responserelationshipbetween pages 1-2): Jirapitcha Boonpor, Solange Parra-Soto, Fanny Petermann-Rocha, Nathan Lynskey, Verónica Cabanas-Sánchez, Naveed Sattar, Jason M. R. Gill, Paul Welsh, Jill P. Pell, Stuart R. Gray, Frederick K. Ho, and Carlos Celis-Morales. Dose–response relationship between device-measured physical activity and incident type 2 diabetes: findings from the uk biobank prospective cohort study. BMC Medicine, May 2023. URL: https://doi.org/10.1186/s12916-023-02851-5, doi:10.1186/s12916-023-02851-5. This article has 38 citations and is from a domain leading peer-reviewed journal.
(khan2023combinationofmultiple pages 1-3): Tauseef A. Khan, David Field, Victoria Chen, Suleman Ahmad, Sonia Blanco Mejia, Hana Kahleová, Dario Rahelić, Jordi Salas-Salvadó, Lawrence A. Leiter, Matti Uusitupa, Cyril W.C. Kendall, and John L. Sievenpiper. Combination of multiple low-risk lifestyle behaviors and incident type 2 diabetes: a systematic review and dose-response meta-analysis of prospective cohort studies. Diabetes Care, 46:643-656, Feb 2023. URL: https://doi.org/10.2337/dc22-1024, doi:10.2337/dc22-1024. This article has 77 citations and is from a highest quality peer-reviewed journal.
(khan2023combinationofmultiple pages 8-10): Tauseef A. Khan, David Field, Victoria Chen, Suleman Ahmad, Sonia Blanco Mejia, Hana Kahleová, Dario Rahelić, Jordi Salas-Salvadó, Lawrence A. Leiter, Matti Uusitupa, Cyril W.C. Kendall, and John L. Sievenpiper. Combination of multiple low-risk lifestyle behaviors and incident type 2 diabetes: a systematic review and dose-response meta-analysis of prospective cohort studies. Diabetes Care, 46:643-656, Feb 2023. URL: https://doi.org/10.2337/dc22-1024, doi:10.2337/dc22-1024. This article has 77 citations and is from a highest quality peer-reviewed journal.
(care20242.diagnosisand pages 2-2): D Care. 2. diagnosis and classification of diabetes: standards of care in diabetes—2024. Unknown journal, 2024.
(care20242.diagnosisand pages 8-9): D Care. 2. diagnosis and classification of diabetes: standards of care in diabetes—2024. Unknown journal, 2024.
(care20242.diagnosisand pages 3-4): D Care. 2. diagnosis and classification of diabetes: standards of care in diabetes—2024. Unknown journal, 2024.
(elmusharaf2025acostof pages 1-2): Khalifa Elmusharaf, Maisoon Mairghani, Sébastien Poix, Emil Scaria, P. Phyo, Win Thu, S. Slama, Matilda Byström, Hicham El Berri, and A. Hammerich. A cost of illness study of the economic burden of diabetes in the eastern mediterranean region. Eastern Mediterranean Health Journal, Aug 2025. URL: https://doi.org/10.26719/2025.31.7.426, doi:10.26719/2025.31.7.426. This article has 6 citations and is from a peer-reviewed journal.
(park2025diabetesfactsheets pages 1-3): Se Eun Park, Seung-Hyun Ko, Ji Yoon Kim, Kyuho Kim, Joon Ho Moon, Nam Hoon Kim, Kyung Do Han, Sung Hee Choi, and Bong Soo Cha. Diabetes fact sheets in korea 2024. Diabetes & Metabolism Journal, 49:24-33, Jan 2025. URL: https://doi.org/10.4093/dmj.2024.0818, doi:10.4093/dmj.2024.0818. This article has 92 citations and is from a peer-reviewed journal.
(care20242.diagnosisand media a404318a): D Care. 2. diagnosis and classification of diabetes: standards of care in diabetes—2024. Unknown journal, 2024.
(care20242.diagnosisand pages 2-3): D Care. 2. diagnosis and classification of diabetes: standards of care in diabetes—2024. Unknown journal, 2024.
(care20242.diagnosisand media aa0d0872): D Care. 2. diagnosis and classification of diabetes: standards of care in diabetes—2024. Unknown journal, 2024.
(rivera2024cardiovascularandrenal pages 1-2): Frederick Berro Rivera, Linnaeus Louisse A. Cruz, John Vincent Magalong, Jade Monica Marie J. Ruyeras, John Paul Aparece, Nathan Ross B. Bantayan, Kyla Lara-Breitinger, and Martha Gulati. Cardiovascular and renal outcomes of glucagon-like peptide 1 receptor agonists among patients with and without type 2 diabetes mellitus: a meta-analysis of randomized placebo-controlled trials. American Journal of Preventive Cardiology, 18:100679, Jun 2024. URL: https://doi.org/10.1016/j.ajpc.2024.100679, doi:10.1016/j.ajpc.2024.100679. This article has 126 citations and is from a peer-reviewed journal.
(colhoun2024longtermkidneyoutcomes pages 1-2): Helen M. Colhoun, Ildiko Lingvay, Paul M. Brown, John Deanfield, Kirstine Brown-Frandsen, Steven E. Kahn, Jorge Plutzky, Koichi Node, Alexander Parkhomenko, Lars Rydén, John P. H. Wilding, Johannes F. E. Mann, Katherine R. Tuttle, Thomas Idorn, Naveen Rathor, and A. Michael Lincoff. Long-term kidney outcomes of semaglutide in obesity and cardiovascular disease in the select trial. Nature Medicine, 30:2058-2066, May 2024. URL: https://doi.org/10.1038/s41591-024-03015-5, doi:10.1038/s41591-024-03015-5. This article has 238 citations and is from a highest quality peer-reviewed journal.
(reichman2025stemcellderivedfully pages 1-2): Trevor W. Reichman, James F. Markmann, Jon Odorico, Piotr Witkowski, John J. Fung, Martin Wijkstrom, Fouad Kandeel, Eelco J.P. de Koning, Anne L. Peters, Chantal Mathieu, Leslie S. Kean, Bote G. Bruinsma, Chenkun Wang, Molly Mascia, Bastiano Sanna, Gautham Marigowda, Felicia Pagliuca, Doug Melton, Camillo Ricordi, and Michael R. Rickels. Stem cell-derived, fully differentiated islets for type 1 diabetes. The New England journal of medicine, Jun 2025. URL: https://doi.org/10.1056/nejmoa2506549, doi:10.1056/nejmoa2506549. This article has 129 citations and is from a highest quality peer-reviewed journal.
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Genetic Associations

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Environmental Factors

Biochemical Markers

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Related Datasets

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References & Deep Research

References

Deep Research

Disease Characteristics Research Template

Target Disease

Research Objectives

1. Disease Information

2. Etiology

3. Phenotypes

4. Genetic/Molecular Information

5. Environmental Information

6. Mechanism / Pathophysiology

7. Anatomical Structures Affected

8. Temporal Development

9. Inheritance and Population

10. Diagnostics

11. Outcome/Prognosis

12. Treatment

13. Prevention

14. Other Species / Natural Disease

15. Model Organisms

Citation Requirements

Output Format

Comprehensive Disease Characteristics Report: Diabetes Mellitus (Complex disease)

Executive summary (current understanding)

Evidence and citation note

1. Disease information

1.1 What is the disease?

1.2 Key identifiers

1.3 Common synonyms / alternative names

1.4 Data provenance

2. Etiology

2.1 Disease causal factors (high level)

2.2 Risk factors

Genetic risk factors (examples)

Environmental/lifestyle risk factors (T2D)

2.3 Protective factors

2.4 Gene–environment interactions

3. Phenotypes

3.1 Core phenotypes (symptoms/signs/lab abnormalities)

3.2 Chronic complications (high level)

3.3 Suggested HPO terms (non-exhaustive, for knowledge base population)

4. Genetic / molecular information

4.1 Causal genes

4.2 Pathogenic variants / allele frequency / somatic vs germline

4.3 Epigenetic information

5. Environmental information

5.1 Lifestyle factors

5.2 Infectious agents

6. Mechanism / pathophysiology

6.1 Causal chains (simplified)

T1D

T2D

6.2 Key pathways/processes (ontology-mappable suggestions)

6.3 Key cell types (CL suggestions)

6.4 Key anatomical sites (UBERON suggestions)

7. Anatomical structures affected

8. Temporal development

9. Inheritance and population

9.1 Epidemiology (recent statistics)

9.2 Genetic architecture

10. Diagnostics

10.1 Guideline diagnostic criteria (ADA Standards of Care)

10.2 Biomarkers for classification