This AOP is licensed under a Creative Commons Attribution 4.0 International License.

Aop: 465

Title

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AhR activation leads to increased diabetes risk

Short name

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Graphical Representation

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Authors

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Ma. Enrica Angela Ching

Point of Contact

The user responsible for managing the AOP entry in the AOP-KB and controlling write access to the page by defining the contributors as described in the next section. More help

Arthur Author (email point of contact)

Contributors

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Arthur Author

Status

Provides users with information concerning how actively the AOP page is being developed, what type of use or input the authors feel comfortable with given the current level of development, and whether it is part of the OECD AOP Development Workplan and has been reviewed and/or endorsed. OECD Status - Tracks the level of review/endorsement the AOP has been subjected to. OECD Project Number - Project number is designated and updated by the OECD. SAAOP Status - Status managed and updated by SAAOP curators. More help

Author status	OECD status	OECD project	SAAOP status
Under development: Not open for comment. Do not cite

This AOP was last modified on August 18, 2022 18:01

Revision dates for related pages

Page	Revision Date/Time
Activation, AhR	March 22, 2018 14:00
Up Regulation, CYP1A1	August 18, 2022 11:18
Oxidative Stress	July 15, 2022 09:40
Increase, Mitochondrial Dysfunction	February 09, 2022 11:48
Pancreatic beta cell dysfunction	August 18, 2022 17:55
Increased diabetes risk	August 18, 2022 18:32
Activation, AhR leads to Up Regulation, CYP1A1	December 03, 2016 16:37
Up Regulation, CYP1A1 leads to Increase, Mt Dysfunction	August 18, 2022 17:06
Up Regulation, CYP1A1 leads to Oxidative Stress	August 08, 2022 22:45
Up Regulation, CYP1A1 leads to Beta cell dysfunction	August 08, 2022 22:46
Oxidative Stress leads to Increase, Mt Dysfunction	August 08, 2022 22:45
Activation, AhR leads to Increased diabetes risk	August 08, 2022 22:47
Increase, Mt Dysfunction leads to Beta cell dysfunction	August 08, 2022 22:45
Oxidative Stress leads to Beta cell dysfunction	August 15, 2022 10:56
Beta cell dysfunction leads to Increased diabetes risk	August 08, 2022 22:45
2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)	February 09, 2017 14:32
benzo[a]pyrene	October 30, 2019 16:47
Polycyclic aromatic hydrocarbons (PAHs)	February 09, 2017 15:43
Dioxin and dioxin-like compounds	November 29, 2016 21:19
Bioflavonoids	April 25, 2017 04:07
Tryptophan derivatives	August 08, 2022 22:56

Abstract

A concise and informative summation of the AOP under development that can stand-alone from the AOP page. The aim is to capture the highlights of the AOP and its potential scientific and regulatory relevance. More help

Diabetes is a chronic disease that affects nearly half a billion people globally (International Diabetes Association, 2021). Type 2 diabetes is the most common type of diabetes and accounts for 90-95% of all cases (American Diabetes Association, 2021; International Diabetes Association, 2021). The recent increase in T2D incidence and prevalence cannot be explained by genetic predisposition alone, and exposure to environmental pollutants have been shown to increase diabetes risk (Henriksen et al., 1997; Lee et al., 2006, 2010; Wolf et al., 2019).

The aryl hydrocarbon receptor (AhR) is a transcription factor known to respond to a broad range of environmental cues, including exposure to persistent organic pollutants (Stejskalova et al., 2011; Stockinger et al., 2014). Strong epidemiological evidence suggests environmental pollutant exposure is associated with type 2 diabetes (Airaksinen et al., 2011; Henriksen et al., 1997; Lee et al., 2006). Serum AhR ligand activity has also been shown to be higher in individuals with T2D and is associated with insulin resistance (Roh et al., 2015). In mice, knocking out AhR led to increased insulin sensitivity and glucose tolerance compared to wildtype mice (Wang et al., 2011).

One of the many target genes of the AhR is cytochrome P4501A1 (CYP1A1), which codes for a xenobiotic metabolism enzyme. CYP1A1 is inducible and functional in human and mouse islets after exposure to pollutants (Ibrahim et al., 2020). Although CYP1A1 is important in detoxification, it is known to produce reactive oxygen species (ROS) as a by-product of its activity (Shimada & Fujii-Kuriyama, 2004). Beta cells may be especially susceptible to ROS, because they produce relatively lower levels of antioxidants (Gurgul-Convey et al., 2016). In fact, excess production of ROS is associated with mitochondrial damage and beta cell dysfunction (Li et al., 2009). INS-1E cells (pancreatic beta cell line) and rat islets acutely exposed to an oxidative stress agent (200 µM H2O2 for 10 minutes) exhibited reduced glucose-induced insulin secretion and decreased oxygen consumption 3 days post-exposure. Treated INS-1E cells also had increased mitochondrial ROS compared to untreated cells (Li et al., 2009).

AOP Development Strategy

Context

Used to provide background information for AOP reviewers and users that is considered helpful in understanding the biology underlying the AOP and the motivation for its development.The background should NOT provide an overview of the AOP, its KEs or KERs, which are captured in more detail below. More help

This AOP was developed to show how environmental pollutant exposure can increase diabetes risk via the AhR pathway.

Strategy

Provides a description of the approaches to the identification, screening and quality assessment of the data relevant to identification of the key events and key event relationships included in the AOP or AOP network.This information is important as a basis to support the objective/envisaged application of the AOP by the regulatory community and to facilitate the reuse of its components. Suggested content includes a rationale for and description of the scope and focus of the data search and identification strategy/ies including the nature of preliminary scoping and/or expert input, the overall literature screening strategy and more focused literature surveys to identify additional information (including e.g., key search terms, databases and time period searched, any tools used). More help

Summary of the AOP

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Events:

Molecular Initiating Events (MIE)

An MIE is a specialised KE that represents the beginning (point of interaction between a prototypical stressor and the biological system) of an AOP. More help

Key Events (KE)

A measurable event within a specific biological level of organisation. More help

Adverse Outcomes (AO)

An AO is a specialized KE that represents the end (an adverse outcome of regulatory significance) of an AOP. More help

Type	Event ID	Title	Short name

MIE

Activation, AhR

KE	80	Up Regulation, CYP1A1	Up Regulation, CYP1A1
KE	1392	Oxidative Stress	Oxidative Stress
KE	1968	Increase, Mitochondrial Dysfunction	Increase, Mt Dysfunction
KE	2037	Pancreatic beta cell dysfunction	Beta cell dysfunction

2038

Increased diabetes risk

Relationships Between Two Key Events (Including MIEs and AOs)

This table summarizes all of the KERs of the AOP and is populated in the AOP-Wiki as KERs are added to the AOP.Each table entry acts as a link to the individual KER description page. More help

Title	Adjacency	Evidence	Quantitative Understanding

Activation, AhR leads to Up Regulation, CYP1A1	adjacent	Moderate	Moderate
Up Regulation, CYP1A1 leads to Oxidative Stress	adjacent	Moderate	Low
Oxidative Stress leads to Increase, Mt Dysfunction	adjacent	Moderate	Low
Increase, Mt Dysfunction leads to Beta cell dysfunction	adjacent	Moderate	Low
Beta cell dysfunction leads to Increased diabetes risk	adjacent	Moderate	Moderate

Up Regulation, CYP1A1 leads to Increase, Mt Dysfunction	non-adjacent	Moderate	Low
Up Regulation, CYP1A1 leads to Beta cell dysfunction	non-adjacent	Low	Low
Activation, AhR leads to Increased diabetes risk	non-adjacent	Moderate	Low
Oxidative Stress leads to Beta cell dysfunction	non-adjacent	Moderate	Low

Network View

This network graphic is automatically generated based on the information provided in the MIE(s), KEs, AO(s), KERs and Weight of Evidence (WoE) summary tables. The width of the edges representing the KERs is determined by its WoE confidence level, with thicker lines representing higher degrees of confidence. This network view also shows which KEs are shared with other AOPs. More help

Prototypical Stressors

A structured data field that can be used to identify one or more “prototypical” stressors that act through this AOP. Prototypical stressors are stressors for which responses at multiple key events have been well documented. More help

Name
2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)
benzo[a]pyrene
Polycyclic aromatic hydrocarbons (PAHs)
Dioxin and dioxin-like compounds
Bioflavonoids
Tryptophan derivatives

Life Stage Applicability

The life stage for which the AOP is known to be applicable. More help

Life stage	Evidence
All life stages	Low

Taxonomic Applicability

Latin or common names of a species or broader taxonomic grouping (e.g., class, order, family) can be selected.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. More help

Term	Scientific Term	Evidence	Link
Mus musculus	Mus musculus	High	NCBI
humans	Homo sapiens	High	NCBI

Sex Applicability

The sex for which the AOP is known to be applicable. More help

Sex	Evidence
Mixed	High

Overall Assessment of the AOP

Addressess the relevant biological domain of applicability (i.e., in terms of taxa, sex, life stage, etc.) and Weight of Evidence (WoE) for the overall AOP as a basis to consider appropriate regulatory application (e.g., priority setting, testing strategies or risk assessment). More help

The empirical evidence for the overall AOP is low, but biological plausibility is moderate.

Domain of Applicability

Addressess the relevant biological domain(s) of applicability in terms of sex, life-stage, taxa, and other aspects of biological context. More help

This AOP is applicable to all animals susceptible to diabetes, including humans and non-human primates, rodents, and some birds (Niaz et al., 2018; Walker et al., 2021). Diabetes onset can occur across all life stages (International Diabetes Association, 2021).

Essentiality of the Key Events

The essentiality of KEs can only be assessed relative to the impact of manipulation of a given KE (e.g., experimentally blocking or exacerbating the event) on the downstream sequence of KEs defined for the AOP. Consequently, evidence supporting essentiality is assembled on the AOP page, rather than on the independent KE pages that are meant to stand-alone as modular units without reference to other KEs in the sequence. The nature of experimental evidence that is relevant to assessing essentiality relates to the impact on downstream KEs and the AO if upstream KEs are prevented or modified. This includes: Direct evidence: directly measured experimental support that blocking or preventing a KE prevents or impacts downstream KEs in the pathway in the expected fashion. Indirect evidence: evidence that modulation or attenuation in the magnitude of impact on a specific KE (increased effect or decreased effect) is associated with corresponding changes (increases or decreases) in the magnitude or frequency of one or more downstream KEs. More help

Cyp1 knockout studies in mice suggest Cyp1a1 is essential to mitochondrial injury caused by AhR pathway activation. Wildtype mice exposed to β-Naphthoflavone, an AhR agonist, had reduced hepatic mitochondrial oxygen consumption rate (OCR) compared to Cyp1a1/1a2 double knockout (DKO) mice and Cyp1a1/1a2/1b1 triple knockout (TKO) mice (Anandasadagopan et al., 2017). Another group of researchers looked at the effects of benzo[a]pyrene (BaP) and TCDD on pancreatic mitochondrial function and found similar results (Ghosh et al., 2018). Compared to wildtype mice, DKO and TKO mice were protected from BaP-induced mitochondrial dysfunction (Ghosh et al., 2018). Interestingly, mice with only Cyp1b1 knocked out were not protected against BaP-induced mitochondrial dysfunction, but DKO and TKO mice had similar protection (Ghosh et al., 2018). They also showed that in mouse pancreatic tissue, BaP induced Cyp1a1 and Cyp1b1, but not Cyp1a2 (Ghosh et al., 2018). This suggests that the observed phenotype is mainly driven by Cyp1a1.

Evidence Assessment

Addressess the biological plausibility, empirical support, and quantitative understanding from each KER in an AOP. More help

Evidence for each KER is low to moderate (Table 1). Overall, the biological plausibility of the AOP is moderate, but the empirical evidence is low.

Known Modulating Factors

Modulating factors (MFs) may alter the shape of the response-response function that describes the quantitative relationship between two KES, thus having an impact on the progression of the pathway or the severity of the AO.The evidence supporting the influence of various modulating factors is assembled within the individual KERs. More help

Quantitative Understanding

Optional field to provide quantitative weight of evidence descriptors. More help

The quantitative understanding for the overall AOP is low.

Considerations for Potential Applications of the AOP (optional)

Addressess potential applications of an AOP to support regulatory decision-making.This may include, for example, possible utility for test guideline development or refinement, development of integrated testing and assessment approaches, development of (Q)SARs / or chemical profilers to facilitate the grouping of chemicals for subsequent read-across, screening level hazard assessments or even risk assessment. More help

References

List of the literature that was cited for this AOP. More help

Airaksinen, R., Rantakokko, P., Eriksson, J. G., Blomstedt, P., Kajantie, E., & Kiviranta, H. (2011). Association between type 2 diabetes and exposure to persistent organic pollutants. Diabetes Care, 34, 1972–1979. https://doi.org/10.2337/dc10-2303

American Diabetes Association. (2021). Classification and diagnosis of diabetes: Standards of medical care in diabetes-2021. Diabetes Care, 44(Supplement 1), S15–S33. https://doi.org/10.2337/dc21-S002

Anandasadagopan, S. K., Singh, N. M., Raza, H., Bansal, S., Selvaraj, V., Singh, S., Chowdhury, A. R., Leu, N. A., & Avadhani, N. G. (2017). β-naphthoflavone-induced mitochondrial respiratory damage in Cyp1 knockout mouse and in cell culture systems: Attenuation by resveratrol treatment. Oxidative Medicine and Cellular Longevity, 2017. https://doi.org/10.1155/2017/5213186

Bindokas, V. P., Kuznetsov, A., Sreenan, S., Polonsky, K. S., Roe, M. W., & Philipson, L. H. (2003). Visualizing Superoxide Production in Normal and Diabetic Rat Islets of Langerhans *. Journal of Biological Chemistry, 278(11), 9796–9801. https://doi.org/10.1074/JBC.M206913200

Ghosh, J., Chowdhury, A. R., Srinivasan, S., Chattopadhyay, M., Bose, M., Bhattacharya, S., Raza, H., Fuchs, S. Y., Rustgi, A. K., Gonzalez, F. J., & Avadhani, N. G. (2018). Cigarette smoke toxins-induced mitochondrial dysfunction and pancreatitis involves aryl hydrocarbon receptor mediated Cyp1 gene expression: Protective effects of resveratrol. Toxicological Sciences, 166(2), 428–440. https://doi.org/10.1093/toxsci/kfy206

Gurgul-Convey, E., Mehmeti, I., Plötz, T., Jörns, A., & Lenzen, S. (2016). Sensitivity profile of the human EndoC-βH1 beta cell line to proinflammatory cytokines. Diabetologia, 59(10), 2125–2133. https://doi.org/10.1007/S00125-016-4060-Y/FIGURES/5

Henriksen, G. L., Ketchum, N. S., Michalek, J. E., & Swaby, J. A. (1997). Serum dioxin and diabetes mellitus in veterans of Operation Ranch Hand. Epidemiology, 8(3), 252–258. https://doi.org/10.1097/00001648-199705000-00005

Ibrahim, M., MacFarlane, E. M., Matteo, G., Hoyeck, M. P., Rick, K. R. C., Farokhi, S., Copley, C. M., O’Dwyer, S., & Bruin, J. E. (2020). Functional cytochrome P450 1A enzymes are induced in mouse and human islets following pollutant exposure. Diabetologia, 63, 162–178. https://doi.org/10.1007/s00125-019-05035-0

International Diabetes Association. (2021). IDF Diabetes Atlas, 10th edition. www.diabetesatlas.org

Lee, D. H., Lee, I. K., Song, K., Steffes, M., Toscano, W., Baker, B. A., & Jacobs, D. R. (2006). A strong dose-response relation between serum concentrations of persistent organic pollutants and diabetes: Results from the National Health and Examination Survey 1999-2002. Diabetes Care, 29(7), 1638–1644. https://doi.org/10.2337/dc06-0543

Lee, D. H., Steffes, M. W., Sjödin, A., Jones, R. S., Needham, L. L., & Jacobs, D. R. (2010). Low dose of some persistent organic pollutants predicts type 2 diabetes: A nested case-control study. Environmental Health Perspectives, 118(9), 1235–1242. https://doi.org/10.1289/ehp.0901480

Li, N., Brun, T., Cnop, M., Cunha, D. A., Eizirik, D. L., & Maechler, P. (2009). Transient Oxidative Stress Damages Mitochondrial Machinery Inducing Persistent β-Cell Dysfunction. The Journal of Biological Chemistry, 284(35), 23602. https://doi.org/10.1074/JBC.M109.024323

Roh, E., Kwak, S. H., Jung, H. S., Cho, Y. M., Pak, Y. K., Park, K. S., Kim, S. Y., & Lee, H. K. (2015). Serum aryl hydrocarbon receptor ligand activity is associated with insulin resistance and resulting type 2 diabetes. Acta Diabetologica, 52(3), 489–495. https://doi.org/10.1007/S00592-014-0674-Z/TABLES/4

Shimada, T., & Fujii-Kuriyama, Y. (2004). Metabolic activation of polycyclic aromatic hydrocarbons to carcinogens by cytochromes P450 1A1 and 1B1. In Cancer Science (Vol. 95, Issue 1, pp. 1–6). https://doi.org/10.1111/j.1349-7006.2004.tb03162.x

Stejskalova, L., Dvorak, Z., & Pavek, P. (2011). Endogenous and exogenous ligands of aryl hydrocarbon receptor: Current state of art. Current Drug Metabolism, 12(2), 198–212. https://doi.org/10.2174/138920011795016818

Stockinger, B., Meglio, P. di, Gialitakis, M., & Duarte, J. H. (2014). The aryl hydrocarbon receptor: Multitasking in the immune system. Annual Review of Immunology, 32, 403–432. https://doi.org/10.1146/annurev-immunol-032713-120245

Wang, C., Xu, C. X., Krager, S. L., Bottum, K. M., Liao, D. F., & Tischkau, S. A. (2011). Aryl hydrocarbon receptor deficiency enhances insulin sensitivity and reduces PPAR α pathway activity in mice. Environmental Health Perspectives, 119(12), 1739–1744. https://doi.org/10.1289/ehp.1103593

Wolf, K., Bongaerts, B. W. C., Schneider, A., Huth, C., Meisinger, C., Peters, A., Schneider, A., Wittsiepe, J., Schramm, K. W., Greiser, K. H., Hartwig, S., Kluttig, A., & Rathmann, W. (2019). Persistent organic pollutants and the incidence of type 2 diabetes in the CARLA and KORA cohort studies. Environment International, 129, 221–228. https://doi.org/10.1016/j.envint.2019.05.030