Tailored, Multimodal Therapy Best for Alzheimer Disease

Treatment regimens for Alzheimer disease (AD) should be tailored to the individual, as the results from a recent study indicate that risk factors for the disease “comprise a network of interlocking feedback loops that may be modifiable.”

Previous single-treatment studies have nearly all failed to treat AD, according to the researchers. They suggest that this failure is due to the fact that AD is “a complex disease with multiple underlying drivers contributing to risk, onset, and progression.”

In order to examine the efficacy of a multi-therapy approach based on disease risk factors, the researchers designed software in order to execute a precision-medicine-based approach to develop treatment strategies to slow or reverse biologic drivers of AD.

Their study included individuals with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). They included genomic data, biospecimen measurements, imaging data, medical histories, medications, allergies, comorbidities, relevant lifestyle factors, and results of neuropsychology testing in their analysis.

The software used algorithms to create a personalized care plan for each individual. After an average time of 8.4 months on their treatment plans, 80% of the participants demonstrated improved memory function scores or remained steady, as measured by standardized cognitive evaluations.

“Our findings indicate previously unidentified connectivity between AD risk factors, suggesting that treatment regimens should be tailored to the individual and multi-modal to simultaneously return several risk factors to a normative state. If successfully performed, the possibility to slow progression of AD and possibly reverse aspects of cognitive decline may become achievable.”

—Michael Potts

Reference:

JKeine D, Walker JQ, Kennedy BK, Sabbagh MN. Development, application, and results from a precision-medicine platform that personalizes multi-modal treatment plans for mild Alzheimer’s disease and at-risk individuals [published online November 2018]. Current Aging Science. doi : 10.2174/1874609811666181019101430.