AI model predicts biological aging using steroid pathways

Researchers have developed a new method to predict biological aging using artificial intelligence. This technique uses a deep neural network (DNN) that focuses on steroid hormone pathways. Biological aging refers to the physical changes that occur over time due to cellular damage. This process can increase the risk of diseases like Alzheimer's and Parkinson's. Unlike chronological aging, which is simply how old a person is, biological aging gives a better understanding of the aging process itself. Current ways to measure biological aging are complex and often not very accurate. Many of these methods depend on physical traits like grip strength. Recent research has shifted towards using blood tests and other scientific measures but still lacks precision. In their study, researchers created a DNN model based on steroid pathways to improve accuracy in predicting biological aging. They studied data from 150 individuals, including healthy participants aged from 20 to 73. The model used sophisticated techniques to consider different factors, including sex and individual health. The results showed that certain steroids, like cortisol, can serve as important indicators of biological aging. Cortisol is known as the stress hormone and is linked to several biological processes. The study found that it positively correlates with biological age. Notably, the model revealed differences between how aging pathways operate in men and women. For example, stress-related hormones and other steroids impacted aging in distinct ways for each sex. Additionally, men who smoke showed signs of accelerated biological aging compared to non-smokers, highlighting the health risks of smoking. This new DNN model offers a promising tool for understanding the complex nature of aging. Researchers hope to refine it further by including more diverse data and other health indicators in future studies.