Physiological variability in key Alzheimer's biomarkers in amyloid-positive clinical trial cohorts and the mechanistic basis of biomarker ratios

Background: Protein biomarkers in cerebrospinal fluid (CSF) and plasma have established themselves as essential tools for the diagnosis of neurological disorders and for disease monitoring, thanks to their accuracy and clinical validity. Outside their original intended context of use, protein biomarkers are increasingly used in clinical trials to assess the effects of novel therapies on brain biology. Physiological differences across individuals - such as CSF or plasma volume and elimination kinetics - also contribute to biomarker variability. A deeper understanding of these sources of variability is essential to further improve biomarker interpretation, particularly in clinical trials where populations are by design more homogeneous than in real-world diagnostic settings. This study aimed to quantify the contributions of physiological factors to inter-individual biomarker variability, and to identify the mechanistic basis for why ratio-based normalization can lead to enhanced biomarker performance. Methods: Using a mechanistic kinetic framework and paired CSF and plasma baseline data from four Phase III clinical trials (GRADUATE I and II, CREAD and CREAD2), we quantified physiological and neurobiological contributions (hereafter, physiological and neurobiological variability) to inter-individual variability of commonly used AD biomarkers (Aβ40, Aβ42, p-tau181, t-tau, NfL, GFAP, sTREM2, YKL-40), and evaluated the conditions under which ratio-based normalization can reduce physiological variability. Results: In these amyloid-positive clinical-trial populations, a large fraction of inter-individual variability could be attributed to physiological variability. While normalizing biomarker values by Aβ40 or Aβ42 reduced physiological variability for some biomarkers, the effects of the normalization were compartment-, and cohort-dependent. Using our framework, we identified two conditions under which ratio-based normalization is most likely to improve biomarker performance: (1) the target and reference biomarkers strongly share physiological variability, and (2) they maintain independent neurobiological variability. These conditions provide a mechanistic explanation for why Aβ-based ratios are useful for some biomarkers but are not optimal for others. Discussion: These findings advance our understanding of the sources of biomarker variability in clinical trials and provide a framework for better understanding the mechanistic basis of ratio-based normalization. This work aims to strengthen the utility of using biomarkers by clarifying when and why ratio-based approaches are most informative.