To the extent possible under law, AOP-Wiki has waived all copyright and related or neighboring rights to KE:1873

Event: 1873

Key Event Title

The KE title should describe a discrete biological change that can be measured. It should generally define the biological object or process being measured and whether it is increased, decreased, or otherwise definably altered relative to a control state. For example “enzyme activity, decreased”, “hormone concentration, increased”, or “growth rate, decreased”, where the specific enzyme or hormone being measured is defined. More help

Type 2 diabetes

Short name
The KE short name should be a reasonable abbreviation of the KE title and is used in labelling this object throughout the AOP-Wiki. The short name should be less than 80 characters in length. More help

Biological Context

Structured terms, selected from a drop-down menu, are used to identify the level of biological organization for each KE. Note, KEs should be defined within a particular level of biological organization. Only KERs should be used to transition from one level of organization to another. Selection of the level of biological organization defines which structured terms will be available to select when defining the Event Components (below). More help

Key Event Components

Further information on Event Components and Biological Context may be viewed on the attached pdf.Because one of the aims of the AOP-KB is to facilitate de facto construction of AOP networks through the use of shared KE and KER elements, authors are also asked to define their KEs using a set of structured ontology terms (Event Components). In the absence of structured terms, the same KE can readily be defined using a number of synonymous titles (read by a computer as character strings). In order to make these synonymous KEs more machine-readable, KEs should also be defined by one or more “event components” consisting of a biological process, object, and action with each term originating from one of 22 biological ontologies (Ives, et al., 2017; See List). Biological process describes dynamics of the underlying biological system (e.g., receptor signalling). The biological object is the subject of the perturbation (e.g., a specific biological receptor that is activated or inhibited). Action represents the direction of perturbation of this system (generally increased or decreased; e.g., ‘decreased’ in the case of a receptor that is inhibited to indicate a decrease in the signalling by that receptor).Note that when editing Event Components, clicking an existing Event Component from the Suggestions menu will autopopulate these fields, along with their source ID and description. To clear any fields before submitting the event component, use the 'Clear process,' 'Clear object,' or 'Clear action' buttons. If a desired term does not exist, a new term request may be made via Term Requests. Event components may not be edited; to edit an event component, remove the existing event component and create a new one using the terms that you wish to add. More help

Key Event Overview

AOPs Including This Key Event

All of the AOPs that are linked to this KE will automatically be listed in this subsection. This table can be particularly useful for derivation of AOP networks including the KE. Clicking on the name of the AOP will bring you to the individual page for that AOP. More help
AOP Name Role of event in AOP Point of Contact Author Status OECD Status
AChE inhibition leading to T2D AdverseOutcome Arthur Author (send email) Under development: Not open for comment. Do not cite


This is a structured field used to identify specific agents (generally chemicals) that can trigger the KE. Stressors identified in this field will be linked to the KE in a machine-readable manner, such that, for example, a stressor search would identify this as an event the stressor can trigger. NOTE: intermediate or downstream KEs in one AOP may function as MIEs in other AOPs, meaning that stressor information may be added to the KE description, even if it is a downstream KE in the pathway currently under development.Information concerning the stressors that may trigger an MIE can be defined using a combination of structured and unstructured (free-text) fields. For example, structured fields may be used to indicate specific chemicals for which there is evidence of an interaction relevant to this MIE. By linking the KE description to a structured chemical name, it will be increasingly possible to link the MIE to other sources of chemical data and information, enhancing searchability and inter-operability among different data-sources and knowledgebases. The free-text section “Evidence for perturbation of this MIE by stressor” can be used both to identify the supporting evidence for specific stressors triggering the MIE as well as to define broad chemical categories or other properties that classify the stressors able to trigger the MIE for which specific structured terms may not exist. More help

Taxonomic Applicability

Latin or common names of a species or broader taxonomic grouping (e.g., class, order, family) can be selected from an ontology. In many cases, individual species identified in these structured fields will be those for which the strongest evidence used in constructing the AOP was available in relation to this KE. More help
Term Scientific Term Evidence Link
humans Homo sapiens High NCBI

Life Stages

The structured ontology terms for life-stage are more comprehensive than those for taxa, but may still require further description/development and explanation in the free text section. More help
Life stage Evidence
3 to < 6 years Moderate
6 to < 11 years Moderate
11 to < 16 years Moderate
16 to < 21 years Moderate
Adult High

Sex Applicability

The authors must select from one of the following: Male, female, mixed, asexual, third gender, hermaphrodite, or unspecific. More help
Term Evidence
Male High
Female High

Key Event Description

A description of the biological state being observed or measured, the biological compartment in which it is measured, and its general role in the biology should be provided. For example, the biological state being measured could be the activity of an enzyme, the expression of a gene or abundance of an mRNA transcript, the concentration of a hormone or protein, neuronal activity, heart rate, etc. The biological compartment may be a particular cell type, tissue, organ, fluid (e.g., plasma, cerebrospinal fluid), etc. The role in the biology could describe the reaction that an enzyme catalyses and the role of that reaction within a given metabolic pathway; the protein that a gene or mRNA transcript codes for and the function of that protein; the function of a hormone in a given target tissue, physiological function of an organ, etc. Careful attention should be taken to avoid reference to other KEs, KERs or AOPs. Only describe this KE as a single isolated measurable event/state. This will ensure that the KE is modular and can be used by other AOPs, thereby facilitating construction of AOP networks. More help

Type 2 diabetes (T2D) is a chronic metabolic disease characterized by hyperglycemia, impaired insulin secretion and/or insulin resistance (2). Living with diabetes negatively impacts quality of life and is associated with a myriad of diseases including cardiovascular (e.g., stroke, myocardial infarction), kidney, neuropathy, vascular (e.g., amputation), amongst others (14). There are several known factors that contribute to T2D risk including lifestyle and genetics (21), as well as chemical exposure (9, 13). The inability of the endocrine pancreas to respond to nutrient signals and a decrease in the anabolic effect of insulin at target tissues are established aetiologies associated with T2D.

In the former, the β-cells of the pancreatic islets of Langerhans are dysfunctional and fail to robustly secrete insulin in response to changes to nutrient levels, primarily glucose. Dysfunction of the β-cell can occur for a variety of reasons including chemical insult (8, 12) and genetic polymorphisms (20). While the early aetiology T2D is often associated with compensatory hyperinsulinemia to manage hyperglycemia, many become hypoinsulinemic in the later stages of the disease as β-cells fail to adequately secrete insulin and/or undergo apoptosis (6) leading to insulin dependence.

In the latter, the effects of insulin at target tissues including hepatic, skeletal muscle, and adipose tissue are impaired (16). Insulin resistance decreases glucose uptake in skeletal muscle and adipose tissue. Excess glucose in many cases is shunted to the liver where it promotes lipid accumulation. In turn, this increases inflammation in adipose tissue resulting in increased lipolysis. The subsequent increase in adipose tissue lipolysis releases more lipids into circulation that accumulate in the liver and furthers the progression of T2D (16).

How It Is Measured or Detected

One of the primary considerations in evaluating AOPs is the relevance and reliability of the methods with which the KEs can be measured. The aim of this section of the KE description is not to provide detailed protocols, but rather to capture, in a sentence or two, per method, the type(s) of measurements that can be employed to evaluate the KE and the relative level of scientific confidence in those measurements. Methods that can be used to detect or measure the biological state represented in the KE should be briefly described and/or cited. These can range from citation of specific validated test guidelines, citation of specific methods published in the peer reviewed literature, or outlines of a general protocol or approach (e.g., a protein may be measured by ELISA).Key considerations regarding scientific confidence in the measurement approach include whether the assay is fit for purpose, whether it provides a direct or indirect measure of the biological state in question, whether it is repeatable and reproducible, and the extent to which it is accepted in the scientific and/or regulatory community. Information can be obtained from the OECD Test Guidelines website and the EURL ECVAM Database Service on Alternative Methods to Animal Experimentation (DB-ALM). ?

Primary diagnostic criteria:

  • Blood glucose: Fasting blood glucose of ≥126 mg/dl (7.0 mmol/l) and a blood glucose of ≥200 mg/dl (11.1 mmol/l) 2 hours following a bolus of glucose are characteristic of T2D (1).
  • Glycated hemoglobin (A1C test): Percent glycated hemoglobin (A1C) ≥6.5% is characteristic of T2D (1).

Other factors:

  • Bodyweight: Weight loss (1). While obesity is used as a screening tool for T2D (7), unexplained significant weight loss is associated with dysfunctional insulin secretion/function which are characteristic of type 1 diabetes and T2D.
  • Blood pressure: Hypertension (15).
  • Lipids: Diabetic pathology is associated with dyslipidemia. Individuals living with T2D are more likely to have elevated triglycerides and decreased high-density lipoprotein cholesterol (5).
  • Eating/drinking: Polydipsia, polyuria, polyphagia (1).

Domain of Applicability

This free text section should be used to elaborate on the scientific basis for the indicated domains of applicability and the WoE calls (if provided). While structured terms may be selected to define the taxonomic, life stage and sex applicability (see structured applicability terms, above) of the KE, the structured terms may not adequately reflect or capture the overall biological applicability domain (particularly with regard to taxa). Likewise, the structured terms do not provide an explanation or rationale for the selection. The free-text section on evidence for taxonomic, life stage, and sex applicability can be used to elaborate on why the specific structured terms were selected, and provide supporting references and background information.  More help

Taxonomy: Some species of mammals can develop diabetic-like pathology including rodents, cats, dogs, pigs, and non-human primates (11). As defined in this AOP, T2D is a human disease.

Life Stages: While T2D is traditionally defined as an adult-onset disease (1), children as young as 4 years old have been diagnosed in Canada (17) and rates of child T2D have been increasing globally (18).

Sex: While T2D is more common in men, it also occurs in women (19).

Regulatory Significance of the Adverse Outcome

An AO is a specialised KE that represents the end (an adverse outcome of regulatory significance) of an AOP. For KEs that are designated as an AO, one additional field of information (regulatory significance of the AO) should be completed, to the extent feasible. If the KE is being described is not an AO, simply indicate “not an AO” in this section.A key criterion for defining an AO is its relevance for regulatory decision-making (i.e., it corresponds to an accepted protection goal or common apical endpoint in an established regulatory guideline study). For example, in humans this may constitute increased risk of disease-related pathology in a particular organ or organ system in an individual or in either the entire or a specified subset of the population. In wildlife, this will most often be an outcome of demographic significance that has meaning in terms of estimates of population sustainability. Given this consideration, in addition to describing the biological state associated with the AO, how it can be measured, and its taxonomic, life stage, and sex applicability, it is useful to describe regulatory examples using this AO. More help

Approximately 1 in 3 Canadians live with diabetes or pre-diabetes (22) and rates of diabetes are higher among vulnerable populations like First Nations individuals (10).

Living with diabetes negatively impacts quality of life and is associated with a myriad of diseases including cardiovascular (e.g., stroke, myocardial infarction), kidney, neuropathy, vascular (e.g., amputation), amongst others (14). Further, pre-existing parental T2D negatively impacts health outcomes in pregnancy for the parent and child including increased risk caesarian section, pre-term delivery, and congenital defects (3). Further, Canadian health care costs associated with diabetes over the last 10 years is estimated to be $15.36 billion (4).

Given these data, T2D and its associated negative health outcomes are of substantial significance to Canadian regulators. Efforts to increase awareness and screening for T2D are encouraged along with access to primary health care for vulnerable populations.


List of the literature that was cited for this KE description. Ideally, the list of references, should conform, to the extent possible, with the OECD Style Guide ( (OECD, 2015). More help

1.          ADA (American Diabetes Association). American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 33 Suppl 1: S62–S69, 2010. doi: 10.2337/dc10-S062.

2.          ADA (American Diabetes Association). Diagnosis and Classification of Diabetes Mellitus. Diabetes Care 37: S81 LP-S90, 2014. doi: 10.2337/dc14-S081.

3.          Alexopoulos A-S, Blair R, Peters AL. Management of Preexisting Diabetes in Pregnancy: A Review. JAMA 321: 1811–1819, 2019. doi: 10.1001/jama.2019.4981.

4.          Bilandzic A, Rosella L. The cost of diabetes in Canada over 10 years: applying attributable health care costs to a diabetes incidence prediction model TT  - Les coûts du diabète sur 10 ans au Canada : intégration des coûts en soins de santé imputables au diabète à un modèle de p. Heal Promot chronic Dis Prev Canada  Res policy Pract 37: 49–53, 2017. doi: 10.24095/hpcdp.37.2.03.

5.          Diabetes Canada. Clinical Practice Guidelines for the Prevention and Management of Diabetes in Canada. Can J Diabetes 42: S1–S325, 2018.

6.          Donath MY, Ehses JA, Maedler K, Schumann DM, Ellingsgaard H, Eppler E, Reinecke M. Mechanisms of β-Cell Death in Type 2 Diabetes. Diabetes 54: S108 LP-S113, 2005. doi: 10.2337/diabetes.54.suppl_2.S108.

7.          Eckel RH, Kahn SE, Ferrannini E, Goldfine AB, Nathan DM, Schwartz MW, Smith RJ, Smith SR. Obesity and type 2 diabetes: what can be unified and what needs to be individualized? J Clin Endocrinol Metab 96: 1654–1663, 2011. doi: 10.1210/jc.2011-0585.

8.          Fabricio G, Malta A, Chango A, De Freitas Mathias PC. Environmental contaminants and pancreatic beta-cells. JCRPE J Clin Res Pediatr Endocrinol 8: 257–263, 2016. doi: 10.4274/jcrpe.2812.

9.          Fénichel P, Chevalier N. Environmental endocrine disruptors: New diabetogens? Comptes Rendus - Biol 340: 446–452, 2017. doi: 10.1016/j.crvi.2017.07.003.

10.        Harris SB, Bhattacharyya O, Dyck R, Hayward MN, Toth EL. Type 2 Diabetes in Aboriginal Peoples. Can J Diabetes 37: S191–S196, 2013. doi: 10.1016/j.jcjd.2013.01.046.

11.        Harwood  Jr HJ, Listrani P, Wagner JD. Nonhuman primates and other animal models in diabetes research. J Diabetes Sci Technol 6: 503–514, 2012. doi: 10.1177/193229681200600304.

12.        Hectors TLM, Vanparys C, Van Der Ven K, Martens GA, Jorens PG, Van Gaal LF, Covaci A, De Coen W, Blust R. Environmental pollutants and type 2 diabetes: A review of mechanisms that can disrupt beta cell function. Diabetologia 54: 1273–1290, 2011. doi: 10.1007/s00125-011-2109-5.

13.        Lee DH, Porta M, Jacobs DR, Vandenberg LN. Chlorinated persistent organic pollutants, obesity, and type 2 diabetes. Endocr Rev 35: 557–601, 2014. doi: 10.1210/er.2013-1084.

14.        Papatheodorou K, Banach M, Bekiari E, Rizzo M, Edmonds M. Complications of Diabetes 2017. J Diabetes Res 2018: 3086167, 2018. doi: 10.1155/2018/3086167.

15.        Petrie JR, Guzik TJ, Touyz RM. Diabetes, Hypertension, and Cardiovascular Disease: Clinical Insights and Vascular Mechanisms. Can J Cardiol 34: 575–584, 2018. doi: 10.1016/j.cjca.2017.12.005.

16.        Samuel VT, Shulman GI. The pathogenesis of insulin resistance: integrating signaling pathways and substrate flux. J Clin Invest 126: 12–22, 2016. doi: 10.1172/JCI77812.

17.        Sawatsky L, Halipchuk J, Wicklow B. Type 2 diabetes in a four-year-old child. CMAJ 189: E888–E890, 2017. doi: 10.1503/cmaj.170259.

18.        Temneanu OR, Trandafir LM, Purcarea MR. Type 2 diabetes mellitus in children and adolescents: a relatively new clinical problem within pediatric practice [Online]. J Med Life 9: 235–239, 2016.

19.        Tramunt B, Smati S, Grandgeorge N, Lenfant F, Arnal J-F, Montagner A, Gourdy P. Sex differences in metabolic regulation and diabetes susceptibility. Diabetologia 63: 453–461, 2020. doi: 10.1007/s00125-019-05040-3.

20.        Witka BZ, Oktaviani DJ, Marcellino M, Barliana MI, Abdulah R. Type 2 Diabetes-Associated Genetic Polymorphisms as Potential Disease Predictors. Diabetes Metab Syndr Obes 12: 2689–2706, 2019. doi: 10.2147/DMSO.S230061.

21.        Wu Y, Ding Y, Tanaka Y, Zhang W. Risk factors contributing to type 2 diabetes and recent advances in the treatment and prevention. Int J Med Sci 11: 1185–1200, 2014. doi: 10.7150/ijms.10001.

22.        Diabetes Canada [Online]. 2019.,-yet-knowledge-of-risk-and-complicatio.