Beyond Statistics: A New Combinatorial Approach to Identifying Biomarker Panels for the Early Detection and Diagnosis of Alzheimer's Disease

Abstract

Interventions to delay or slow Alzheimer's disease (AD) progression are most effective when implemented at pre-clinical disease stages, making early diagnosis essential. For this reason, there is an increasing focus on discovery of predictive biomarkers for AD. Currently, the most reliable predictive biomarkers require either expensive (brain imaging) or invasive (cerebrospinal fluid collection) procedures, leading researchers to strive toward identifying robust biomarkers in blood. Yet promising early results from candidate blood biomarker studies are being refuted by subsequent findings in other cohorts or using different assay technologies. Recent evidence suggests that univariate blood biomarkers are not sufficiently sensitive or specific for the diagnosis of disorders as complex, multifactorial, and heterogeneous as AD. To overcome these present limitations, more consideration must be given to the development of ‘biomarker panels’ assessing multiple molecular entities. The selection of such panels should draw not only on traditional statistical approaches, whether parametric or non-parametric, but also on newer non-statistical approaches that have the capacity to retain and utilize information about all individual study participants rather than collapsing individual data into group summary values (e.g., mean, variance). These new approaches, facilitated by advances in computing, have the potential to preserve the context of interrelationships between different molecular entities, making them amenable to the development of panels that, as a multivariate collective, can overcome the challenge of individual variability and disease heterogeneity to accurately predict and classify AD. We argue that the AD research community should take fuller advantage of these approaches to accelerate discovery.