New research reveals that people who eat the most ultra-processed foods show significantly elevated levels of high-sensitivity C-reactive protein (hs-CRP) – a key marker of inflammation and a strong predictor of cardiovascular disease. The risk is particularly pronounced among adults aged 50 to 59, smokers, and individuals with unhealthy body weights. Surprisingly, physical activity didn’t appear to offset this effect: researchers found no significant difference in hs-CRP levels between sedentary individuals and those meeting exercise guidelines.

A team of researchers at the Icahn School of Medicine at Mount Sinai has developed a new method to identify and reduce biases in datasets used to train machine-learning algorithms—addressing a critical issue that can affect diagnostic accuracy and treatment decisions. The findings were published in the September 4 online issue of the Journal of Medical Internet Research [DOI: 10.2196/71757]. To tackle the problem, the investigators developed AEquity, a tool that helps detect and correct bias in health care datasets before they are used to train artificial intelligence (AI) and machine-learning models. The investigators tested AEquity on different types of health data, including medical images, patient records, and a major public health survey, the National Health and Nutrition Examination Survey, using a variety of machine-learning models. The tool was able to spot both well-known and previously overlooked biases across these datasets.