Consensus on the key characteristics of metabolism disruptors
Metabolism-disrupting agents (MDAs) are chemical, infectious or physical agents that increase the risk of metabolic disorders. Examples include pharmaceuticals, such as antidepressants, and environmental agents, such as bisphenol A. Various types of studies can provide evidence to identify MDAs, yet...
| Autores: | , , , , , , , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universidad Miguel Hernández de Elche |
| Repositorio: | REDIUMH. Depósito Digital de la UMH |
| OAI Identifier: | oai:dspace.umh.es:11000/37999 |
| Acceso en línea: | https://hdl.handle.net/11000/37999 |
| Access Level: | acceso abierto |
| Palabra clave: | Dyslipidaemias Metabolic syndrome Obesity Risk factors Type 2 diabetes |
| Sumario: | Metabolism-disrupting agents (MDAs) are chemical, infectious or physical agents that increase the risk of metabolic disorders. Examples include pharmaceuticals, such as antidepressants, and environmental agents, such as bisphenol A. Various types of studies can provide evidence to identify MDAs, yet a systematic method is needed to integrate these data to help to identify such hazards. Inspired by work to improve hazard identification of carcinogens using key characteristics (KCs), we developed 12 KCs of MDAs based on our knowledge of processes underlying metabolic diseases and the effects of their causal agents: (1) alters function of the endocrine pancreas; (2) impairs function of adipose tissue; (3) alters nervous system control of metabolic function; (4) promotes insulin resistance; (5) disrupts metabolic signalling pathways; (6) alters development and fate of metabolic cell types; (7) alters energy homeostasis; (8) causes inappropriate nutrient handling and partitioning; (9) promotes chronic inflammation and immune dysregulation in metabolic tissues; (10) disrupts gastrointestinal tract function; (11) induces cellular stress pathways; and (12) disrupts circadian rhythms. In this Consensus Statement, we present the logic that revealed the KCs of MDAs and highlight evidence that supports the identification of KCs. We use chemical, infectious and physical agents as examples to illustrate how the KCs can be used to organize and use mechanistic data to help to identify MDAs. |
|---|