"AI's Early Detection of Alzheimer's Disease Risk"

Researchers at UC San Francisco have developed a machine learning model that can predict the onset of Alzheimer’s disease up to seven years before symptoms appear by analyzing electronic health records, identifying high cholesterol and osteoporosis, particularly in women, as significant predictors. The study, published in Nature Aging, highlights the potential of AI to revolutionize early diagnosis and understanding of complex diseases like Alzheimer’s, offering the possibility of earlier intervention and new therapeutic strategies. The research leveraged extensive electronic health databases and identified several top predictors of Alzheimer’s, including hypertension, high cholesterol, and vitamin D deficiency. The study also uncovered a gender-specific link between osteoporosis and Alzheimer’s, paving the way for targeted research into the molecular pathways involved in the disease.