Key Points:
- An advanced predictive model for Alzheimer’s utilized patient records to identify Alzheimer’s co-occurring conditions, achieving predictive accuracies with AUC scores up to 0.81.
- Identified predictive conditions for Alzheimer’s, including hypertension and high cholesterol, with gender-specific factors like erectile dysfunction for men and osteoporosis for women.
- Employed SPOKE to reveal genetic linkages between Alzheimer’s and conditions like hyperlipidemia and osteoporosis, highlighting the potential of precision medicine in early disease prediction and personalized healthcare.
Researchers have developed a method using electronic health records from the University of California, San Francisco, coupled with knowledge networks like SPOKE, to create an advanced predictive model for Alzheimer’s Disease (AD) and explore the biological underpinnings of the disease, including the impact of gender differences.
Advanced Predictive Model for Alzheimer’s
The investigation utilized UCSF’s vast clinical database, encompassing over 5 million patient records, to identify conditions co-occurring with Alzheimer’s diagnosis. By employing random forest models on a cohort of 749 individuals with Alzheimer’s Disease and 250,545 controls, they achieved predictive accuracies with a mean area under the receiver operating characteristic curve ranging from 0.72 (seven years before onset) to 0.81 (one day before onset).
Identifying conditions predictive of Alzheimer’s Disease
This approach not only predicts the onset of Alzheimer’s disease but prioritizes biological hypotheses by identifying conditions predictive of it before its onset. It also contextualizes sex dimorphism in its progression. Results showed factors such as hypertension, high cholesterol, and vitamin D deficiency were found to be predictive in both sexes. And erectile dysfunction and enlarged prostate also relevant for men and osteoporosis for women.
Identifying Genetic linkage
To delve into the biological underpinnings of these predictions, the researchers employed public molecular databases and UCSF’s SPOKE (Scalable Precision Medicine Oriented Knowledge Engine). SPOKE revealed associations between Alzheimer’s and high cholesterol and between osteoporosis and Alzheimer’s in women through genetic variants. Knowledge networks facilitated the discovery of genes shared between Alzheimer’s disease and its top predictors, like APOE and MS4A6A. Thus highlighting the genetic linkage between hyperlipidemia, osteoporosis, and Alzheimer’s Disease.
Implications
This innovative use of clinical data opens avenues for early AD prediction and the development of personalized medical insights. The team hopes this methodology can be applied to other complex diseases. They highlight the role of precision medicine and patient data in advancing the understanding and treatment of conditions like Alzheimer’s.
Reference
- Tang, Alice S., Katherine P. Rankin, Gabriel Cerono, Silvia Miramontes, Hunter Mills, Jacquelyn Roger, Billy Zeng, et al. “Leveraging Electronic Health Records and Knowledge Networks for Alzheimer’s Disease Prediction and Sex-Specific Biological Insights.” Nature Aging, February 21, 2024, 1–17. https://doi.org/10.1038/s43587-024-00573-8.
- Scalable Precision Medicine Open Knowledge Engine. “SPOKE” Accessed March 4, 2024. https://spoke.ucsf.edu/home.
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