In a pioneering study published in Nature Aging, researchers have made significant strides in predicting Alzheimer’s disease (AD) onset by leveraging vast amounts of electronic health records (EHRs) and applying knowledge networks to highlight sex-specific biological insights. This innovative approach, led by the team at the University of California, San Francisco (UCSF), aims to identify individuals at risk of developing AD early on, facilitating timely interventions and potentially slowing the progression of this neurodegenerative disorder.Â
Alzheimer’s disease, the most prevalent form of dementia in those over the age of 65, is posing an increasing problem for healthcare systems throughout the world since it is difficult to diagnose and presently has no treatment. The use of electronic health data in this study has significantly improved our ability to understand and forecast the illness, shedding fresh light on the pathophysiology of Alzheimer’s disease (AD) by highlighting sex-specific biochemical variables. Â
Prediction models were created utilizing data from more than 2.9 million people’s electronic health records. The study focused on Alzheimer’s patients examined for dementia at UC San Francisco’s Memory and Aging Center. After assessing clinical factors and separating the data by gender, the researchers determined that men and women exhibit different AD risk features. The models can predict the development of Alzheimer’s disease up to seven years in advance, highlighting their potential as low-cost, first-line applications in primary care settings for early risk stratification.Â
Because of intrinsic gender variations in susceptibility and resistance, sex is an essential biological element in the Alzheimer’s disease (AD) variety. Women had a higher connection with a decreased risk of Alzheimer’s disease and several clinical indicators. This conclusion emphasizes the importance of including gender in Alzheimer’s disease research and therapy options.Â
Furthermore, the study stressed using heterogeneous knowledge networks, which mix various data sources with decades of scientific research. These networks help us understand the biological basis of Alzheimer’s disease by linking clinical signs and identifying similar genes and pathways. Finding genetic links to the ailment, including the APOE locus, which is known to increase the risk of AD, and verifying that hyperlipidemia and osteoporosis are the major causes of Alzheimer’s disease in external electronic health record databases are critical components of the study.Â
Electronic health records (EHRs) can help us better understand Alzheimer’s disease (AD), and this pioneering study paves the way for sex-specific research and individualized pharmacological treatments for neurodegenerative disorders. This study improves patient outcomes and quality of life for those affected by Alzheimer’s disease (AD) by merging clinical and biochemical data. This, in turn, contributes to the development of new treatments and a better understanding of the problem. Â
Journal Reference – Tang, A. S., Rankin, K. P., Cerono, G., Miramontes, S., Mills, H., Roger, J., … Sirota, M. (2024). Leveraging electronic health records and knowledge networks for Alzheimer’s disease prediction and sex-specific biological insights. Retrieved from https://www.nature.com/articles/s43587-024-00573-8Â
