Blood-Based Biomarkers for Alzheimer's Disease Maintain Robust Performance Across Socioeconomic Landscapes
It is no secret that an individual's overall health and well-being are connected to their life circumstances. A broad spectrum of environmental and background factors are increasingly being recognized as contributors to risk and progression of certain diseases such as Alzheimer's (AD) and related dementias. Neighborhood characteristics such as education, income, employment, and housing quality, can be ranked and quantified using the Area Deprivation Index (ADI) which is calculated from Census group data. ADI has been linked to health conditions such as cardiovascular diseases, chronic stress, kidney disease, obesity, and diabetes - all of which are well-established risk factors for AD and related dementias. However, a direct link between neighborhood-level characteristics and neuropathology is less clear due to conflicting studies.
The progression of Alzheimer's disease may span decades, and cognitive symptoms occur after the hallmark pathologies of amyloid-beta (Aβ) plaques, followed by tau tangles, and finally neurodegeneration in the brain. Early diagnosis has been a strong research focus in recent years. In fact, several clinical trials have targeted Aβ plaques (anti-amyloid treatments) to prevent, or significantly slow, disease progression altogether. Being able to accurately detect AD-specific forms of these proteins in the blood has been groundbreaking. A simple blood test can accurately capture AD-specific blood-based biomarkers presumably before they have begun to aggregate. Having a robust and accurate blood test available creates a viable option for communities that may not have access to neuroimaging.
Alison Myoraku led a study analyzing the association of neighborhood disadvantage with AD and the stability of blood biomarker performance. She used ADI and blood biomarker information from the most current dataset in the Alzheimer's Disease Neuroimaging Initiative (ADNI). This dataset, ADNI4, is the fourth phase in a longitudinal study cohort that has spanned 20 years. ADNI4 was designed to increase generalizability of findings through recruitment efforts in different types of communities nationwide.
Demographic and clinical characteristics from over 400 individuals were included in this study; the cohort was divided into three groups based on ADI information.
The distribution and breakdown of ADI groups was analyzed, along with clinical data such as body mass index (BMI), indicators for kidney (eGFR), vascular, and other metabolic functions. The ADI groups differed by sex, ethnoracial background, and MMSE scores (used for cognitive function). The intermediately disadvantaged group was found to be 1.6x more likely to be Aβ(+) (presence of plaques in the brain conformed by PET neuroimaging) compared to the least disadvantaged group.
Importantly, the predictive accuracy of the three leading blood biomarkers for AD (p-tau217/Aβ42, p-tau217, Aβ42/40) did not differ across groups, confirming that their diagnostic performance is not compromised by neighborhood-level disadvantage.
Models that tested biomarker predictive performance focused on accuracy, sensitivity, and specificity. The p-tau217/Aβ42 ratio stands out as a reliable and robust tool for predicting brain amyloidosis across different socioeconomic landscapes. The clinical utility of these blood biomarkers increases confidence in their use and supports their role in facilitating a more accessible approach to AD diagnosis and early treatment.
The original article "Association of Neighborhood Disadvantage with Alzheimer's Disease Pathology and the Stability of Blood-Based Biomarker Performance" was published in JPAD (Journal for Prevention of Alzheimer's Disease) and can be accessed here.
Collaborators P. Murali Doraiswamy and Laura Wang are at the Duke Center for the Study of Aging and the Duke Institute for Brain Sciences.
This study was presented at an AD-focused clinical trials conference (CTAD) in December of 2025.