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Relationship: 3246

Title

A descriptive phrase which clearly defines the two KEs being considered and the sequential relationship between them (i.e., which is upstream, and which is downstream). More help

Recruitment of inflammatory cells leads to Cardiac Heart Failure

Upstream event

The causing Key Event (KE) in a Key Event Relationship (KER). More help

Recruitment of inflammatory cells

Downstream event

The responding Key Event (KE) in a Key Event Relationship (KER). More help

Cardiac Heart Failure

Key Event Relationship Overview

The utility of AOPs for regulatory application is defined, to a large extent, by the confidence and precision with which they facilitate extrapolation of data measured at low levels of biological organisation to predicted outcomes at higher levels of organisation and the extent to which they can link biological effect measurements to their specific causes.Within the AOP framework, the predictive relationships that facilitate extrapolation are represented by the KERs. Consequently, the overall WoE for an AOP is a reflection in part, of the level of confidence in the underlying series of KERs it encompasses. Therefore, describing the KERs in an AOP involves assembling and organising the types of information and evidence that defines the scientific basis for inferring the probable change in, or state of, a downstream KE from the known or measured state of an upstream KE. More help

AOPs Referencing Relationship

AOP Name	Adjacency	Weight of Evidence	Quantitative Understanding	Point of Contact	Author Status	OECD Status
C6R-Derived Protein K7 following Monkeypox infection leads to heart failure	adjacent	High	Moderate	Arthur Author (send email)	Under development: Not open for comment. Do not cite

Taxonomic Applicability

Latin or common names of a species or broader taxonomic grouping (e.g., class, order, family) that help to define the biological applicability domain of the KER.In general, this will be dictated by the more restrictive of the two KEs being linked together by the KER. More help

Term	Scientific Term	Evidence	Link
mammals	mammals	High	NCBI

Sex Applicability

An indication of the the relevant sex for this KER. More help

Sex	Evidence
Mixed	Moderate

Life Stage Applicability

An indication of the the relevant life stage(s) for this KER. More help

Term	Evidence
Adult, reproductively mature	Moderate
Old Age	High

Key Event Relationship Description

Provides a concise overview of the information given below as well as addressing details that aren’t inherent in the description of the KEs themselves. More help

The recruitment of inflammatory cells very important to the pathogenesis of heart failure because it is able to impact the overall cardiac function through inflammatory pathways. (Kastillo et al., 2020). Cardiac injury or stress can trigger the release of chemokines and cytokines. Macrophages and neutrophils will be attracted to the site of damage within the heart muscle but will cause long term detrimental effects. (Halade et al., 2022). Excessive use of inflammatory pathways will cause tissue damage and remodeling and will lead to fibrosis in the heart. (Thomas et al., 2020). An increase in the deposition of collagen will disrupt normal cardiac architecture and lead to an increase in heart failure progression. (Hajj et al., 2018). Cell death occurs after inflammatory cytokines affect cardiomyocytes. Changes to coronary circulation and endothelial function can impact the overall influence of tissue inflammation on heart failure.

Evidence Collection Strategy

Include a description of the approach for identification and assembly of the evidence base for the KER. For evidence identification, include, for example, a description of the sources and dates of information consulted including expert knowledge, databases searched and associated search terms/strings. Include also a description of study screening criteria and methodology, study quality assessment considerations, the data extraction strategy and links to any repositories/databases of relevant references.Tabular summaries and links to relevant supporting documentation are encouraged, wherever possible. More help

Evidence Supporting this KER

Addresses the scientific evidence supporting KERs in an AOP setting the stage for overall assessment of the AOP. More help

The weight of evidence for this KER is high. This is because multiple studies show that there is a direct correlation and dependent change. When there is cardiac cell inflammation, it leads to an increased chance for heart failure. Inflammation and heart failure mutually reinforce one another. Further research into the β-adrenergic system's role and precise inflammation characterization important when designing targeted therapies in cardiology. (Linthout et al., 2017). Clinical trials with anakinra and insights from the CANTOS trial suggest inflammation's role. Challenges remain in translating animal findings to human therapies, necessitating further research into immune cell roles and targeted treatments. (Strassheim et al., 2019).

Biological Plausibility

Addresses the biological rationale for a connection between KEupstream and KEdownstream. This field can also incorporate additional mechanistic details that help inform the relationship between KEs, this is useful when it is not practical/pragmatic to represent these details as separate KEs due to the difficulty or relative infrequency with which it is likely to be measured. More help

The biological plausibility linking inflammation and heart failure stems from cardiac injury or stress. Inflammation can incrase chances of heart failure through myocardial damage, fibrosis, impaired cardiac function, and systemic effects on blood vessels. This leads to cytokine release, immune cell activation, and chronic inflammatory responses. All of these processes lead to the dysfunction of normal heart function and cardiac remodeling.

Empirical Evidence

Provides specific (citable) evidence that supports the idea of a change in the upstream KE (KEupstream) leading to, or being associated with, a subsequent change in the downstream KE (KEdownstream), assuming the perturbation of KEupstream is sufficient. More help

Dose Concordance:

While there is no dose concordance yet, the proposed experiment would be to increase known cardiac biomarkers which cause cardiac cell inflamation. Afterwards, if cardiac failure follows, then dose concordance has been tested.

Temporal Concordance:

A study of male and female humans was conducted over the course of 12 years. During this period, the research participants were monitored to see if their conditions have worsened after they had inflammation in heart tissue. (Arcopinto et al., 2022).

Incidence Concordance:

While there is no incidence concordance yet, the proposed experiment would be to count the number of individuals that have symptoms with cell inflamation. Afterwards, from those individuals with cell inflamation, we would need to count how many develop heart failure.

Uncertainties and Inconsistencies

Addresses inconsistencies or uncertainties in the relationship including the identification of experimental details that may explain apparent deviations from the expected patterns of concordance. More help

There are currently no known qualitative inconsistencies or uncertainties associated with this relationship because of how well estabilished this KER is.

Known modulating factors

This table captures specific information on the MF, its properties, how it affects the KER and respective references.1.) What is the modulating factor? Name the factor for which solid evidence exists that it influences this KER. Examples: age, sex, genotype, diet 2.) Details of this modulating factor. Specify which features of this MF are relevant for this KER. Examples: a specific age range or a specific biological age (defined by...); a specific gene mutation or variant, a specific nutrient (deficit or surplus); a sex-specific homone; a certain threshold value (e.g. serum levels of a chemical above...) 3.) Description of how this modulating factor affects this KER. Describe the provable modification of the KER (also quantitatively, if known). Examples: increase or decrease of the magnitude of effect (by a factor of...); change of the time-course of the effect (onset delay by...); alteration of the probability of the effect; increase or decrease of the sensitivity of the downstream effect (by a factor of...) 4.) Provision of supporting scientific evidence for an effect of this MF on this KER. Give a list of references. More help

Quantitative Understanding of the Linkage

Captures information that helps to define how much change in the upstream KE, and/or for how long, is needed to elicit a detectable and defined change in the downstream KE. More help

The quantitative understanding between inflammation and heart failure involves measuring biomarkers which include TNF-α and IL-6. (Schumacher et al., 2018). Also, inflammatory cells such as neutrophils and macrophages in the blood and heart tissue should be measured as well. If these markers appear at an elevated level, it correlates with an increase in severity for heart failure. Neutrophil-to-lymphocyte ratio (NLR) also provide information regarding the systemic inflammation's role in heart failure progression. (Afari et al., 2016).

Response-response Relationship

Provides sources of data that define the response-response relationships between the KEs. More help

Time-scale

Information regarding the approximate time-scale of the changes in KEdownstream relative to changes in KEupstream (i.e., do effects on KEdownstream lag those on KEupstream by seconds, minutes, hours, or days?). More help

Known Feedforward/Feedback loops influencing this KER

Define whether there are known positive or negative feedback mechanisms involved and what is understood about their time-course and homeostatic limits. More help

Domain of Applicability

A free-text section of the KER description that the developers can use to explain their rationale for the taxonomic, life stage, or sex applicability structured terms. More help

References

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

Arnolda, L. F., Llewellyn-Smith, I. J., & Minson, J. B. (1999). Animal models of heart failure. Australian and New Zealand Journal of Medicine, 29(3), 403–409. https://doi.org/10.1111/J.1445-5994.1999.TB00735.X

Ashley, E. A., & Niebauer, J. (2004). Cardiovascular examination. https://www.ncbi.nlm.nih.gov/books/NBK2213/

Cao, Z., Jia, Y., & Zhu, B. (2019). BNP and NT-proBNP as Diagnostic Biomarkers for Cardiac Dysfunction in Both Clinical and Forensic Medicine. International Journal of Molecular Sciences, 20(8). https://doi.org/10.3390/IJMS20081820

Knowlton, A. A., & Lee, A. R. (2012). Estrogen and the Cardiovascular System. Pharmacology & Therapeutics, 135(1), 54. https://doi.org/10.1016/J.PHARMTHERA.2012.03.007

Lorraine, B., & Lott, C. (2017). MRI Heart (Cardiac MRI) - InsideRadiology. https://www.insideradiology.com.au/cardiac-mri/

Madhavan, M. v., Gersh, B. J., Alexander, K. P., Granger, C. B., & Stone, G. W. (2018). Coronary Artery Disease in Patients ≥80 Years of Age. Journal of the American College of Cardiology, 71(18), 2015–2040. https://doi.org/10.1016/J.JACC.2017.12.068

Malik, A., Brito, D., Vaqar, S., & Chhabra, L. (2023). Congestive Heart Failure. StatPearls. https://www.ncbi.nlm.nih.gov/books/NBK430873/

Mosca, L., Barrett-Connor, E., & Kass Wenger, N. (2011). Sex/Gender Differences in Cardiovascular Disease Prevention What a Difference a Decade Makes. Circulation, 124(19), 2145. https://doi.org/10.1161/CIRCULATIONAHA.110.968792

Oktay, A. A., Paul, T. K., Koch, C. A., & Lavie, C. J. (2023). Diabetes, Cardiomyopathy, and Heart Failure. Endotext. https://www.ncbi.nlm.nih.gov/books/NBK560257/

Omerovic, S., & Jain, A. (2023). Echocardiogram. StatPearls. https://www.ncbi.nlm.nih.gov/books/NBK558940/

Ponzoni, M., Coles, J. G., & Maynes, J. T. (2023). Rodent Models of Dilated Cardiomyopathy and Heart Failure for Translational Investigations and Therapeutic Discovery. International Journal of Molecular Sciences, 24(4). https://doi.org/10.3390/IJMS24043162

Research, N. R. C. (US) and I. of M. (US) C. on the U. of L. A. in B. and B. (1988). Benefits Derived from the Use of Animals. https://www.ncbi.nlm.nih.gov/books/NBK218274/

Sapna, F., Raveena, F., Chandio, M., Bai, K., Sayyar, M., Varrassi, G., Khatri, M., Kumar, S., & Mohamad, T. (2023). Advancements in Heart Failure Management: A Comprehensive Narrative Review of Emerging Therapies. Cureus, 15(10). https://doi.org/10.7759/CUREUS.46486