According to research published in the journal Environmental Health Perspectives, a team of scientists from the Boston University Schools of Medicine and Public Health created machine learning methods with a specific goal in mind.
They claim that these methods identify and characterize chemicals that are disturbing the metabolism. And, they seem to be located very close to where you spend your time very often.
Obesogens in Your Sofa Could Be the Reason for Your Weight Gain?
Spending too much time on your sofa may be linked to weight gain. Although this isn’t a new discovery since physical activity is essential for good health, a sedentary lifestyle may not be the only reason.
Sitting on your sofa for too long actually exposes you to specific chemicals known as obesogens. These chemicals disrupt the metabolism and they’re present in different household objects and in the environment.
As the name itself points out, they may cause changes in the metabolic processes and increase the risk of weight gain by stimulating the formation of fat cells. Research has only recently begun to explore the type of fat cells that are formed.
The types are several due to exposure to various chemicals. According to Dr. Stefano Monti from the Boston University Department of Medicine, knowing this is essential because not every fat cell is made equally.
Namely, the white fat cells store energy and they contribute to obesity. The brown and brown-on-white ones burn energy and decrease obesity. Stefano notes that their work from the past has shown that environmental chemicals are likelier to encourage the formation of white fat cells.
Monti notes that there’s a link between the higher production of environmental chemicals disrupting the metabolism, also known as MDCs, and the quick rise in obesity and metabolic illnesses of people.
The rise in BMI, according to studies, isn’t just a result of a higher intake of calories and lack of energy expenditure. With this in mind, knowing what and where they are is pivotal so that we know how to decrease our exposure to them.
Machine Learning Explained
Machine learning, a type of AI, uses algorithms and data to replicate how humans learn. For example, we repeat stuff in order to learn to perform a task optimally.
Machine learning includes the repetition of a task with the accuracy becoming better and better. In this context, Monti and his colleagues wanted to choose a method that would offer an unbiased and data-driven approach.
Thanks to machine learning, they could learn from previous studies. They profiled more than 60 chemicals with known effects and they used them to train a computer model to be able to predict their power to disrupt the metabolism.
The experiment’s design was built on work done previously by the Carcinogenome project. The goal was to identify possible carcinogens.
Together, these studies are a conceptual and experimental framework on how to apply the screening of a long list of chemicals for their long-term side effects, including their carcinogenicity and their ability to disrupt the metabolism.
What Are the Effects of MDC Exposure?
The team wanted to extend the applications of the study beyond the method’s specifics and predictive abilities. So, they profiled chemicals and involved drugs used to treat metabolic illnesses.
Their methods helped them have a better look at how these drugs affect the metabolism of cells. This data will be used to design better and targeted meds with as few side effects as possible.
Pinpointing a chemical as an MDC is only the first step, according to Monti. They chose two highly ranked predictions, i.e. quinoxyfen and tonalid, two commonly used pesticides.
They concluded their negative side effects in the formation of fat cells. But, additional tests need to be performed for any regulations to be set.
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