Relationship between topological efficiency of white matter structural connectome and plasma biomarkers across the Alzheimer's disease continuum

Both plasma biomarkers and brain network topology have shown great potential in the early diagnosis of Alzheimer's disease (AD). However, the specific associations between plasma AD biomarkers, structural network topology, and cognition across the AD continuum have yet to be fully elucidated. This retrospective study evaluated participants from the Sino Longitudinal Study of Cognitive Decline cohort between September 2009 and October 2022 with available blood samples or 3.0-T MRI brain scans. Plasma biomarker levels were measured using the Single Molecule Array platform, including beta-amyloid (A beta), phosphorylated tau181 (p-tau181), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). The topological structure of brain white matter was assessed using network efficiency. Trend analyses were carried out to evaluate the alterations of the plasma markers and network efficiency with AD progression. Correlation and mediation analyses were conducted to further explore the relationships among plasma markers, network efficiency, and cognitive performance across the AD continuum. Among the plasma markers, GFAP emerged as the most sensitive marker (linear trend: t = 11.164, p = 3.59 x 10(-24); quadratic trend: t = 7.708, p = 2.25 x 10(-13); adjusted R-2 = 0.475), followed by NfL (linear trend: t = 6.542, p = 2.9 x 10(-10); quadratic trend: t = 3.896, p = 1.22 x 10(-4); adjusted R-2 = 0.330), p-tau181 (linear trend: t = 8.452, p = 1.61 x 10(-15); quadratic trend: t = 6.316, p = 1.05 x 10(-9); adjusted R-2 = 0.346) and A beta 42/A beta 40 (linear trend: t = -3.257, p = 1.27 x 10(-3); quadratic trend: t = -1.662, p = 9.76 x 10(-2); adjusted R-2 = 0.101). Local efficiency decreased in brain regions across the frontal and temporal cortex and striatum. The principal component of local efficiency within these regions was correlated with GFAP (Pearson's R = -0.61, p = 6.3 x 10(-7)), NfL (R = -0.57, p = 6.4 x 10(-6)), and p-tau181 (R = -0.48, p = 2.0 x 10(-4)). Moreover, network efficiency mediated the relationship between general cognition and GFAP (ab = -0.224, 95% confidence interval [CI] = [-0.417 to -0.029], p = .0196 for MMSE; ab = -0.198, 95% CI = [-0.42 to -0.003], p = .0438 for MOCA) or NfL (ab = -0.224, 95% CI = [-0.417 to -0.029], p = .0196 for MMSE; ab = -0.198, 95% CI = [-0.42 to -0.003], p = .0438 for MOCA). Our findings suggest that network efficiency mediates the association between plasma biomarkers, specifically GFAP and NfL, and cognitive performance in the context of AD progression, thus highlighting the potential utility of network-plasma approaches for early detection, monitoring, and intervention strategies in the management of AD.