Developing a blood fatty acid-based algorithm as an early predictor of cognitive decline and dementia: Applying machine learning to harmonized data from prospective cohort studies

Alzheimer’s disease (AD), the most common type of dementia, imposes a substantial, global, socioeconomic burden. An estimated 6.5 million Americans aged 65 and older are living with AD, the most prevalent form of dementia. In the US, estimated health-care payments in 2022 for all patients with AD or related dementias

(ADRD) amount to $345 billion. Without the means to identify high risk individuals, many will find care too little and too late: more than half of individuals with dementia or cognitive decline have not been diagnosed. There is a need for accessible and inexpensive early predictive biomarkers of memory loss and/or incident ADRDs to

facilitate the early identification of high-risk individuals, providing the time necessary to make meaningful lifestyle changes to slow or prevent disease progression. This is especially important since markers like tau or beta-amyloid are primarily markers of existing, not impending disease. Emerging evidence suggests that

erythrocyte (RBC) omega-3 fatty acid (FA) levels may serve as an early signal of impending disease up to 5-years before AD/ADRD develops. As a clinical laboratory that specializes in providing FA measurements, interpretation and customized behavioral interventions, OmegaQuant Analytics (OQA) supports a large and

growing customer base of researchers, clinicians, businesses, and individuals. Through a partnership with the Fatty Acid Research Institute (FA expertise; biostatistical support; data access), we propose to develop a highly predictive FA-based profile using an innovative approach leveraging existing prospective cohort data. To

do this, we will determine the extent to which it is possible to predict memory loss and/or incident all-cause dementia from an RBC FA signature. We will harmonize data from 19,922 individuals with assessment of incident all-cause dementia or an assessment of memory (e.g., Wechsler Memory Scale), with complete FA

profile data and with an average of 10+ years of follow-up. We will then apply statistical / machine learning algorithms to determine the extent to which we can predict incident ADRD or a change in memory from FAs, with separate models for high-risk subgroups, including racial/ethnic groups [Blacks, Hispanics]. Results will be

used to create a Fatty Acid Memory Index (FAMI) and Fatty Acid Dementia Index (FADI). We will create consumer-friendly interpretative reports for FAMI and FADI including actionable steps to change dietary FA behaviors to potentially modify memory loss/ dementia risk. We will determine if other clinical laboratories or

clinicians are willing to pay at least $30/test (wholesale price) for each test [Profitability pathway #1 (PP#1)]. We will also determine individual consumers’ willingness to pay OQA directly $50/test (retail price per test) for either FAMI or FADI (PP#2). Proof of concept feasibility will set us up for a larger-scale prospective study and

improved machine-learning/modelling in Phase II. Ultimately, we hope to generate two simple, early-warning tests that will allow for targeted intervention of individuals at high-risk for developing memory loss and/or dementia, ultimately leading to substantial reductions in the prevalence and societal burden of this disease.