A new Mass General Brigham-led study using AI to analyze electronic health records found that long COVID may be twice as common as current estimates, affecting more than 18 million Americans. The findings reveal major gaps in diagnostic coding-based surveillance, with more than 10 million cases likely uncounted and prevalence continuing to grow over time.

Researchers at the Icahn School of Medicine at Mount Sinai have found evidence that people who fall at the extreme high or low ends of certain traits, such as cholesterol, blood glucose, height, and age at menopause, are more likely to have a simple genetic explanation than previously thought. Their findings, reported in the May 27 issue of Nature [https://doi.org/10.1038/s41586-026-10516-5], may lead to new insights into the causes of common diseases. Many traits linked to human health are considered “polygenic,” meaning they are shaped by the combined influence of many common genetic variants, each contributing only a small effect. But the new study explored whether individuals with extreme trait values may instead be influenced by rarer genetic variants that have a much larger impact. The researchers say this possibility could help explain why some individuals develop unusually high or low levels of traits associated with conditions such as diabetes, heart disease, and stroke.