Endocrine disruptors, obesity, and metabolic syndrome
Abstract
The global prevalence of obesity and metabolic syndrome (MetS) is rising worldwide, and increasing evidence suggests that chemical exposures-particularly endocrine disruptors (EDs)-represent a significant contributing factor. EDs can act as obesogens, increasing the risk of weight gain and related metabolic conditions, including type 2 diabetes, dyslipidemia, hypertension, and cardiovascular disease. They may also alter the basal metabolic rate, gut microbiota composition, and hormonal regulation of appetite and satiety. EDs are reported to exert their effects mainly through the peroxisome proliferator-activated receptor gamma pathway, which is primarily expressed in adipose tissue and is a key regulator of adipogenesis. Common consumer products such as plastic bottles, metal food cans, detergents, toys, cosmetics, and pesticides frequently contain EDs. Humans can be exposed to these chemicals via multiple routes, including transplacental transfer, breast milk, inhalation, ingestion, and dermal absorption. Bisphenols, tributyltin, phthalates, per- and polyfluoroalkyl substances, polycyclic aromatic hydrocarbons, and heavy metals are among the known EDs that have been associated with obesity and MetS. The need for further investigation and stricter regulations to mitigate the public health consequences of environmental exposure to EDs is consistently emphasized in recent literature. Understanding the mechanisms by which EDs affect various hormones and systems is essential for…
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.