Women Exhibit Lower Global Left Frontal Cortex Connectivity Among Cognitively Unimpaired Elderly Individuals: A Pilot Study from SILCODE

Innovative Multivariable Model Combining MRI Radiomics and Plasma Indexes Predicts Alzheimer's Disease Conversion: Evidence from a 2-Cohort Longitudinal Study

To explore the complementary relationship between magnetic resonance imaging (MRI) radiomic and plasma biomarkers in the early diagnosis and conversion prediction of Alzheimer's disease (AD), our study aims to develop an innovative multivariable prediction model that integrates those two for predicting conversion results in AD. This longitudinal multicentric cohort study included 2 independent cohorts: the Sino Longitudinal Study on Cognitive Decline (SILCODE) project and the Alzheimer Disease Neuroimaging Initiative (ADNI). We collected comprehensive assessments, MRI, plasma samples, and amyloid positron emission tomography data. A multivariable logistic regression analysis was applied to combine plasma and MRI radiomics biomarkers and generate a new composite indicator. The optimal model's performance and generalizability were assessed across populations in 2 cross-racial cohorts. A total of 897 subjects were included, including 635 from the SILCODE cohort (mean [SD] age, 64.93 [6.78] years; 343 [63%] female) and 262 from the ADNI cohort (mean [SD] age, 73.96 [7.06] years; 140 [53%] female). The area under the receiver operating characteristic curve of the optimal model was 0.9414 and 0.8979 in the training and validation dataset, respectively. A calibration analysis displayed excellent consistency between the prognosis and actual observation. The findings of the present study provide a valuable diagnostic tool for identifying at-risk individuals for AD and highlight the pivotal role of the radiomic biomarker. Importantly, built upon data-driven analyses commonly seen in previous radiomics studies, our research delves into AD pathology to further elucidate the underlying reasons behind the robust predictive performance of the MRI radiomic predictor.

Sex difference in biological change and mechanism of Alzheimer’s disease: from macro- to micro-landscape

Alzheimer's disease (AD) is the most common form of dementia and numerous studies reported a higher prevalence and incidence of AD among women. Although women have longer lifetime, longevity does not wholly explain the higher frequency and lifetime risk in women. It is important to understand sex differences in AD pathophysiology and pathogenesis, which could provide foundation for future clinical AD research. Here, we reviewed the most recent and relevant literature on sex differences in biological change of AD from macroscopical neuroimaging to microscopical pathologic change (neuronal degeneration, synaptic dysfunction, amyloid-beta and tau accumulation). We also discussed sex differences in cellular mechanisms related to AD (neuroinflammation, mitochondria dysfunction, oxygen stress, apoptosis, autophagy, blood-brain-barrier dysfunction, gut microbiome alteration, bulk and single cell/nucleus omics) and possible causes underlying these differences including sex-chromosome, sex hormone and hypothalamic-pituitary- adrenal (HPA) axis effects.

Lifelong experiences as a proxy of cognitive reserve moderate the association between connectivity and cognition in Alzheimer's disease

Alzheimer's disease (AD) is associated with alterations in functional connectivity (FC) of the brain. The FC underpinnings of CR, that is, lifelong experiences, are largely unknown. Resting-state FC and structural MRI were performed in 76 CSF amyloid-β (Aβ) negative healthy controls and 152 Aβ positive individuals as an AD spectrum cohort (ADS; 55 with subjective cognitive decline, SCD; 52 with mild cognitive impairment; 45 with AD dementia). Following a region-of-interest (ROI) FC analysis, intrinsic network connectivity within the default-mode network (INC-DMN) and anti-correlation in INC between the DMN and dorsal attention network (DMN:DAN) were obtained as composite scores. CR was estimated by education and Lifetime Experiences Questionnaire (LEQ). The association between INC-DMN and MEM was attenuated by higher LEQ scores in the entire ADS group, particularly in SCD. In ROI analyses, higher LEQ scores were associated with higher FC within the DMN in ADS group. INC-DMN remains relatively intact despite memory decline in individuals with higher lifetime activity estimates, supporting a role for functional networks in maintaining cognitive function in AD.

β-Amyloid in blood neuronal-derived extracellular vesicles is elevated in cognitively normal adults at risk of Alzheimer's disease and predicts cerebral amyloidosis

Background

Blood biomarkers that can be used for preclinical Alzheimer’s disease (AD) diagnosis would enable trial enrollment at a time when the disease is potentially reversible. Here, we investigated plasma neuronal-derived extracellular vesicle (nEV) cargo in patients along the Alzheimer’s continuum, focusing on cognitively normal controls (NCs) with high brain β-amyloid (Aβ) loads (Aβ+).

Methods

The study was based on the Sino Longitudinal Study on Cognitive Decline project. We enrolled 246 participants, including 156 NCs, 45 amnestic mild cognitive impairment (aMCI) patients, and 45 AD dementia (ADD) patients. Brain Aβ loads were determined using positron emission tomography. NCs were classified into 84 Aβ− NCs and 72 Aβ+ NCs. Baseline plasma nEVs were isolated by immunoprecipitation with an anti-CD171 antibody. After verification, their cargos, including Aβ, tau phosphorylated at threonine 181, and neurofilament light, were quantified using a single-molecule array. Concentrations of these cargos were compared among the groups, and their receiver operating characteristic (ROC) curves were constructed. A subset of participants underwent follow-up cognitive assessment and magnetic resonance imaging. The relationships of nEV cargo levels with amyloid deposition, longitudinal changes in cognition, and brain regional volume were explored using correlation analysis. Additionally, 458 subjects in the project had previously undergone plasma Aβ quantification.

Results

Only nEV Aβ was included in the subsequent analysis. We focused on Aβ₄₂ in the current study. After normalization of nEVs, the levels of Aβ₄₂ were found to increase gradually across the cognitive continuum, with the lowest in the Aβ− NC group, an increase in the Aβ+ NC group, a further increase in the aMCI group, and the highest in the ADD group, contributing to their diagnoses (Aβ− NCs vs. Aβ+ NCs, area under the ROC curve values of 0.663; vs. aMCI, 0.857; vs. ADD, 0.957). Furthermore, nEV Aβ₄₂ was significantly correlated with amyloid deposition, as well as longitudinal changes in cognition and entorhinal volume. There were no differences in plasma Aβ levels among NCs, aMCI, and ADD individuals.

Conclusions

Our findings suggest the potential use of plasma nEV Aβ₄₂ levels in diagnosing AD-induced cognitive impairment and Aβ+ NCs. This biomarker reflects cortical amyloid deposition and predicts cognitive decline and entorhinal atrophy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-022-01010-x.

Exploring brain glucose metabolic patterns in cognitively normal adults at risk of Alzheimer's disease: A cross-validation study with Chinese and ADNI cohorts

OBJECTIVE

Disease-related metabolic brain patterns have been verified for a variety of neurodegenerative diseases including Alzheimer's disease (AD). This study aimed to explore and validate the pattern derived from cognitively normal controls (NCs) in the Alzheimer's continuum.

METHODS

This study was based on two cohorts; one from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the other from the Sino Longitudinal Study on Cognitive Decline (SILCODE). Each subject underwent [18F]fluoro-2-deoxyglucose positron emission tomography (PET) and [18F]florbetapir-PET imaging. Participants were binary-grouped based on β-amyloid (Aβ) status, and the positivity was defined as Aβ+. Voxel-based scaled subprofile model/principal component analysis (SSM/PCA) was used to generate the "at-risk AD-related metabolic pattern (ARADRP)" for NCs. The pattern expression score was obtained and compared between the groups, and receiver operating characteristic curves were drawn. Notably, we conducted cross-validation to verify the robustness and correlation analyses to explore the relationships between the score and AD-related pathological biomarkers.

RESULTS

Forty-eight Aβ+ NCs and 48 Aβ- NCs were included in the ADNI cohort, and 25 Aβ+ NCs and 30 Aβ- NCs were included in the SILCODE cohort. The ARADRPs were identified from the combined cohorts and the two separate cohorts, characterized by relatively lower regional loadings in the posterior parts of the precuneus, posterior cingulate, and regions of the temporal gyrus, as well as relatively higher values in the superior/middle frontal gyrus and other areas. Patterns identified from the two separate cohorts showed some regional differences, including the temporal gyrus, basal ganglia regions, anterior parts of the precuneus, and middle cingulate. Cross-validation suggested that the pattern expression score was significantly higher in the Aβ+ group of both cohorts (p < 0.01), and contributed to the diagnosis of Aβ+ NCs (with area under the curve values of 0.696-0.815). The correlation analysis revealed that the score was related to tau pathology measured in cerebrospinal fluid (p-tau: p < 0.02; t-tau: p < 0.03), but not Aβ pathology assessed with [18F]florbetapir-PET (p > 0.23).

CONCLUSIONS

ARADRP exists for NCs, and the acquired pattern expression score shows a certain ability to discriminate Aβ+ NCs from Aβ- NCs. The SSM/PCA method is expected to be helpful in the ultra-early diagnosis of AD in clinical practice.