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How to Predict Alzheimer’s Years in Advance: EHRs and AI Unlock Answers

Mar 4, 2024

Imagine a world where Alzheimer’s disease (AD) could be predicted years before symptoms appear. This would allow for early interventions and personalized treatment plans. A groundbreaking new study using electronic health records (EHRs), knowledge networks, and artificial intelligence (AI) brings us closer to that possibility. Researchers at the University of California San Francisco Medical Center have developed machine learning models that can accurately predict AD. Their work also highlights significant sex-specific biological differences in AD risk.

The Power of Predictive Models

The most impressive finding? Machine learning models trained on EHR data could predict who would develop AD up to seven years in advance! These models don’t just tell us who is at risk; they also help scientists understand the underlying causes of the disease.

Hyperlipidemia: A Key Risk Factor Across the Board
One of the study’s significant findings was that hyperlipidemia (high cholesterol and other blood fats) is a major risk factor for AD. This was confirmed in the study’s data and other external EHR datasets, adding weight to the finding. Interestingly, the study also found a genetic link between hyperlipidemia and AD at the specific locus in our DNA called the APOE locus.

Sex-Specific Insights: The Role of Osteoporosis
Perhaps the most surprising finding was that osteoporosis (weak bones) appears to be a significant risk factor for AD in women. This was also validated in external EHR data. This discovery suggests there may be considerable biological differences that influence AD development in women, opening doors to tailored prevention and treatment strategies.

Conclusion: AI and Big Data Transform Alzheimer’s Research
This research perfectly demonstrates how analyzing large amounts of healthcare data with advanced AI can fundamentally change our approach to Alzheimer’s disease. By predicting AD early and shedding light on the sex-specific factors at play, we can move toward more effective and personalized interventions that give hope to those affected by this challenging disease.

Resources

  1. National Institute on Aging – Alzheimer’s Disease: https://www.nia.nih.gov/health/alzheimers-disease-fact-sheet 
  2. Alzheimer’s Association: https://www.alz.org/
  3. Apolipoprotein E (APOE) gene: https://www.alzforum.org/mutations/apoe
  4. Osteoporosis and Bone Health: https://www.bones.nih.gov/

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