Using AI to predict Alzheimer's disease

Alzheimer’s disease could be predicted up to seven years before symptoms appear, according to researchers from University of California – San Francisco.

Artificial intelligence can be used to analyse patient records with machine learning, spotting patterns in clinical data.

The researchers used UCSF’s clinical database of more than five million patients to look for co-occurring conditions in patients who had been diagnosed with Alzheimer’s, in comparison to individuals without AD. It was possible for them to identify with 72% predictive power who would develop the disease up to seven years prior.

Several factors, including hypertension, high cholesterol and vitamin D deficiency, were predictive in both men and women. Erectile dysfunction and an enlarged prostate were also predictive for men. But for women, osteoporosis was a particularly important predictor.

The researchers hope that one day AI will hasten the diagnosis and treatment of Alzheimer’s and other complex diseases.

“This is a first step towards using AI on routine clinical data, not only to identify risk as early as possible, but also to understand the biology behind it,” said the study’s lead author, Alice Tang, an MD/PhD student in the Sirota Lab at UCSF.

“The power of this AI approach comes from identifying risk based on combinations of diseases.”

To understand the biology underlying the model’s predictive power, the researchers turned to public molecular databases and a specialised tool developed at UCSF called SPOKE (Scalable Precision Medicine Oriented Knowledge Engine), which was developed in the lab of Sergio Baranzini, PhD, a professor of neurology and a member of the UCSF Weill Institute for Neurosciences.

SPOKE is essentially a database of databases that researchers can use to identify patterns and potential molecular targets for therapy.

It picked up the well-known association between Alzheimer’s and high cholesterol, through a variant form of the apolipoprotein E gene, APOE4.

But, when combined with genetic databases, it also identified a link between osteoporosis and Alzheimer’s in women, through a variant in a lesser-known gene, called MS4A6A.

Ultimately, the researchers hope the approach can be used with other hard-to-diagnose diseases like lupus and endometriosis.

“This is a great example of how we can leverage patient data with machine learning to predict which patients are more likely to develop Alzheimer’s, and also to understand the reasons why that is so,” said the study’s senior author, Marina Sirota, PhD, associate professor at the Bakar Computational Health Sciences Institute at UCSF.

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