Impaired glymphatic function as a biomarker for subjective cognitive decline: An exploratory dual cohort study

Abstract

Background: Subjective cognitive decline (SCD) has been recognized as a potential risk stage for progression to Alzheimer's disease (AD), while glymphatic dysfunction is considered an important characteristic of AD. We hypothesize that glymphatic dysfunction occurs during the SCD stage, aiming to discover potential biomarkers for SCD.

Methods: Participants from two independent studies, Sino Longitudinal Study on Cognitive Decline (SILCODE, n = 654) and the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 650), representing different ethnicities and disease stages, were included to assess glymphatic function using diffusion tensor image analysis along the perivascular space (DTI-ALPS).

Results: Abnormal glymphatic function occurs during the SCD stage, with the ALPS index demonstrating excellent classification performance for SCD and normal controls (area under the receiver operating characteristic curve [AUC]_SILCODE = 0.816, AUC_ADNI = 0.797). Lower ALPS index indicates higher risk of cognitive progression, which is negatively correlated with Subjective Cognitive Decline Questionnaire 9 scores and amyloid positron emission tomography burden.

Disscusion: Our study suggests the ALPS index has the potential to serve as a biomarker for SCD.

Highlights: Glymphatic function characterized by the analysis along the perivascular space (ALPS) index becomes abnormal in subjective cognitive decline (SCD), the earliest symptomatic manifestation and preclinical stage of Alzheimer's disease (AD). The ALPS index demonstrates excellent classification performance for SCD and normal controls in the East Asian and Western cohorts. Participants with a lower ALPS index show a higher risk of clinical progression. The ALPS index is closely associated with serval cognitive scales and amyloid beta burden.

Keywords: Alzheimer's disease; biomarker; diffusion tensor image analysis along the perivascular space; glymphatic; subjective cognitive decline.

FIGURE 1

Image pre‐processing flow and ALPS index calculation. ALPS, analysis along the perivascular space; DTI, diffusion tensor imaging; FA, fractional anisotropy; JHU ICBM DTI‐81 WM atlas, Johns Hopkins University International Consortium of Brain Mapping DTI‐81 White Matter atlas

FIGURE 2

Diffusion images and ROI locations of typical cases from two cohorts. AD, Alzheimer's disease; ADNI, Alzheimer's Disease Neuroimaging Initiative; ALPS, analysis along the perivascular space; edu, education; FA, fractional anisotropy; MCI, mild cognitive impairment; MoCA, Montreal Cognitive Assessment; MoCA‐B, Montreal Cognitive Assessment Basic; NC, normal control; SCD, subjective cognitive decline; SILCODE, Sino Longitudinal Study on Cognitive Decline; y/o, years old

FIGURE 3

Performance of ALPS index for distinguishing NC from SCD and predicting the Aβ status of SCD. ROC results of ALPS index and conventional DTI metrics distinguish NC and SCD in SILCODE (A) and ADNI (B). C, ALPS index and conventional DTI metrics predict the Aβ status of SCD in ADNI. Trends of ALPS index among NC, SCD A−, and SCD A+ groups in the left (D), right (E), and bilateral (F). “−” indicates negative; “+” indicates positive. ^* P < 0.05, ^** P < 0.01, ^*** P < 0.001. A, amyloid; Aβ, amyloid beta; AD, axial diffusivity; ADNI, Alzheimer's Disease Neuroimaging Initiative; ALPS, analysis along the perivascular space; AUC, area under the curve; DTI, diffusion tensor imaging; FA, fractional anisotropy; MD, mean diffusivity; NC, normal control; RD, radial diffusivity; ROC, receiver operating characteristic curve; SCD, subjective cognitive decline; SILCODE, Sino Longitudinal Study on Cognitive Decline

FIGURE 4

Trend of ALPS index along the AD continuum. A–C, Results in SILCODE. D–F, Results in ADNI. ^* P < 0.05, ^** P < 0.01, ^*** P < 0.001. AD, Alzheimer's disease; ADNI, Alzheimer's Disease Neuroimaging Initiative; ALPS, analysis along the perivascular space; MCI, mild cognitive impairment; NC, normal control; SCD, subjective cognitive decline; SILCODE, Sino Longitudinal Study on Cognitive Decline

FIGURE 5

Cox model with hazard ratio and Kaplan–Meier survival curves. Cox model predicting the cognitive decline risk of ALPS index after adjustment for age, years of education, sex in SCD and NC participants (A), (C). Associations of ALPS index with risk of clinical progression, the Kaplan–Meier curves of clinical progression to MCI/AD dementia in SCD and NC participants (B), (D). AD, Alzheimer's disease; AIC, Akaike information criterion; ALPS, analysis along the perivascular space; APOE, apolipoprotein E; edu, education; MCI, mild cognitive impairment; NC, normal control; SCD, subjective cognitive decline.

FIGURE 6

Correlation between ALPS index and neuropsychological tests and Aβ deposition: (A) correlation between ALPS index and SCD‐9 in SILCODE; (B) correlation between ALPS index and MoCA‐B in SILCODE; (C) correlation between ALPS index and MoCA in ADNI; (D) correlation between ALPS index and SUVRs of AV45 in ADNI; (E) voxel‐wise analysis of the association between ALPS index and Aβ PET; (F) mediation effect of Aβ deposition between ALPS index and MoCA scores in ADNI. ^* P < 0.05, ^** P < 0.01. Aβ, amyloid beta; AD, Alzheimer's disease; ADNI, Alzheimer's Disease Neuroimaging Initiative; ALPS, analysis along the perivascular space; AV45: florbetapir; c, direct effect; c', indirect effect; MCI, mild cognitive impairment; MoCA, Montreal Cognitive Assessment; MoCA‐B, Montreal Cognitive Assessment Basic; NC, normal control; PET, positron emission tomography; SCD, subjective cognitive decline; SCD‐9, 9‐item subjective cognitive decline questionnaire; SILCODE, Sino Longitudinal Study on Cognitive Decline; SUVR, standardized uptake value ratio