Alzheimer's disease (AD) is a progressive, neurodegenerative condition in which individuals exhibit memory loss, dementia, and impaired metabolism. Nearly all previous single-domain studies to treat AD have failed, likely because it is a complex disease with multiple underlying drivers contributing to risk, onset, and progression. Keine et al. explored the efficacy of a multidomain therapy approach based on the disease risk factor status specific to individuals with AD diagnosis or concern. Their findings indicate previously unidentified connectivity between AD risk factors, suggesting that treatment regimens should be tailored to the individual, and should be multi-modal to simultaneously return risk factors to a normative state. If successfully performed, the possibility to slow progression of AD and even reverse aspects of cognitive decline may become achievable.
Keine et al. completed analysis for forty subjects with subjective cognitive decline (SCD) and mild cognitive impairment (MCI), using novel software from uMETHOD Health. The software is designed to execute a precision-medicine-based approach to develop personalized treatment recommendations, with the goal of slowing or reversing biologic drivers of AD. AD-associated inputs encompassed genomic data, biospecimen measurements, imaging data (such as MRIs or PET scans), medical histories, medications, allergies, comorbidities, relevant lifestyle factors, and results of neuropsychology testing. Algorithms were employed to prioritize physiologic and lifestyle states with the highest probability of contributing to disease status, and these priorities were incorporated into a personalized care plan, which was delivered to physicians and supported by health coaches to increase adherence. With an average of 8.4 months on their treatment plans (equal to about 2.8 iterations of care plans), 80% of individuals in the study showed overall improved memory function scores or held steady, as measured by cognitive evaluations.
It is increasingly recognized that early intervention is key to developing an effective therapy for AD; a precision-medicine platform enables an actionable multidomain therapy for those in the early stages of the disease. Many of the underlying pathologic drivers of the disease (e.g., high homocysteine, genetic biases, insulin resistance, poor diet, poor sleep, lack of exercise, chronic inflammation, toxicity) are modifiable, allowing persons to reduce their risk and potentially delay disease onset. With a multitude of underlying drivers of AD, genetic factors, comorbidities, medications, and optimizations for each person, the amount of data used in generating a care plan quickly accumulates, making a timely process beyond the scope of what a physician can do by hand, and do well quickly. But where manual methods fail, clinical informatics platforms excel. These platforms can provide a personalized treatment method for each individual in a repeatable, predictable, and timely manner. Applying these complex treatment plans to a broader audience through the development of other disease state-specific algorithms could increase the quality of life for many in the aging population.
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Reference: Keine, Dorothy, et al. Development, application, and results from a precision-medicine platform that personalizes multi-modal treatment plans for mild Alzheimer's disease and at-risk individuals. Current Aging Science, 2018, DOI: 10.2174/1874609811666181019101430
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http://dx.doi.org/10.2174/1874609811666181019101430
