Biomarkers are the only feasible way to detect and monitor presymptomatic Alzheimer's disease (AD). No single biomarker can predict future cognitive decline with an acceptable level of accuracy. In addition to designing powerful multimodal diagnostic platforms, a careful investigation of the major sources of disease heterogeneity and their influence on biomarker changes is needed. Here we investigated the accuracy of a novel multimodal biomarker classifier for differentiating cognitively normal (NC), mild cognitive impairment (MCI) and AD subjects with and without stratification by ApoE4 genotype. 111 NC, 182 MCI and 95 AD ADNI participants provided both structural MRI and CSF data at baseline. We used an automated machine-learning classifier to test the ability of hippocampal volume and CSF Aβ, t-tau and p-tau levels, both separately and in combination, to differentiate NC, MCI and AD subjects, and predict conversion. We hypothesized that the combined hippocampal/CSF biomarker classifier model would achieve the highest accuracy in differentiating between the three diagnostic groups and that ApoE4 genotype will affect both diagnostic accuracy and biomarker selection. The combined hippocampal/CSF classifier performed better than hippocampus-only classifier in differentiating NC from MCI and NC from AD. It also outperformed the CSF-only classifier in differentiating NC vs. AD. Our amyloid marker played a role in discriminating NC from MCI or AD but not for MCI vs. AD. Neurodegenerative markers contributed to accurate discrimination of AD from NC and MCI but not NC from MCI. Classifiers predicting MCI conversion performed well only after ApoE4 stratification. Hippocampal volume and sex achieved AUC = 0.68 for predicting conversion in the ApoE4-positive MCI, while CSF p-tau, education and sex achieved AUC = 0.89 for predicting conversion in ApoE4-negative MCI. These observations support the proposed biomarker trajectory in AD, which postulates that amyloid markers become abnormal early in the disease course while markers of neurodegeneration become abnormal later in the disease course and suggests that ApoE4 could be at least partially responsible for some of the observed disease heterogeneity.

Introduction

Loss of entorhinal cortex (EC) layer II neurons represents the earliest Alzheimer's disease (AD) lesion in the brain. Research suggests differing functional roles between two EC subregions, the anterolateral EC (aLEC) and the posteromedial EC (pMEC).

Methods

We use joint label fusion to obtain aLEC and pMEC cortical thickness measurements from serial magnetic resonance imaging scans of 775 ADNI-1 participants (219 healthy; 380 mild cognitive impairment; 176 AD) and use linear mixed-effects models to analyze longitudinal associations among cortical thickness, disease status, and cognitive measures.

Results

Group status is reliably predicted by aLEC thickness, which also exhibits greater associations with cognitive outcomes than does pMEC thickness. Change in aLEC thickness is also associated with cerebrospinal fluid amyloid and tau levels.

Discussion

Thinning of aLEC is a sensitive structural biomarker that changes over short durations in the course of AD and tracks disease severity-it is a strong candidate biomarker for detection of early AD.

Abstract Background Alzheimer's disease (AD) is associated with deposition of amyloid β (Aβ) in the brain, which is reflected by low concentration of the Aβ1-42 peptide in the cerebrospinal fluid (CSF). There are at least 15 additional Aβ peptides in human CSF and their relative abundance pattern is thought to reflect the production and degradation of Aβ. Here, we test the hypothesis that AD is characterized by a specific CSF Aβ isoform pattern that is distinct when comparing sporadic AD (SAD) and familial AD (FAD) due to different mechanisms underlying brain amyloid pathology in the two disease groups. Results We measured Aβ isoform concentrations in CSF from 18 patients with SAD, 7 carriers of the FAD-associated presenilin 1 (PSEN1) A431E mutation, 17 healthy controls and 6 patients with depression using immunoprecipitation-mass spectrometry. Low CSF levels of Aβ1-42 and high levels of Aβ1-16 distinguished SAD patients and FAD mutation carriers from healthy controls and depressed patients. SAD and FAD were characterized by similar changes in Aβ1-42 and Aβ1-16, but FAD mutation carriers exhibited very low levels of Aβ1-37, Aβ1-38 and Aβ1-39. Conclusion SAD patients and PSEN1 A431E mutation carriers are characterized by aberrant CSF Aβ isoform patterns that hold clinically relevant diagnostic information. PSEN1 A431E mutation carriers exhibit low levels of Aβ1-37, Aβ1-38 and Aβ1-39; fragments that are normally produced by γ-secretase, suggesting that the PSEN1 A431E mutation modulates γ-secretase cleavage site preference in a disease-promoting manner.

Background

APOE is the largest genetic risk factor for Alzheimer's disease (AD), but there is a substantial polygenic component. Polygenic risk scores (PRS) can summarize small effects across the genome but may obscure differential risk across molecular processes and pathways that contribute to heterogeneity of disease presentation.

Objective

We examined polygenic risk impacting specific AD-associated pathways and its relationship with clinical status and biomarkers of amyloid, tau, and neurodegeneration (A/T/N).

Methods

We analyzed data from 1,411 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We applied pathway analysis and clustering to identify AD-associated "pathway clusters" and construct pathway-specific PRSs (excluding the APOE region). We tested associations with diagnostic status, abnormal levels of amyloid and ptau, and hippocampal volume.

Results

Thirteen pathway clusters were identified, and eight pathway-specific PRSs were significantly associated with AD diagnosis. Amyloid-positivity was associated with endocytosis and fibril formation, response misfolded protein, and regulation protein tyrosine PRSs. Ptau positivity and hippocampal volume were both related to protein localization and mitophagy PRS, and ptau-positivity was also associated with an immune signaling PRS. A global AD PRS showed stronger associations with diagnosis and all biomarkers compared to pathway PRSs.

Conclusions

Pathway PRS may contribute to understanding separable disease processes, but do not add significant power for predictive purposes. These findings demonstrate that AD-phenotypes may be preferentially associated with risk in specific pathways, and defining genetic risk along multiple dimensions may clarify etiological heterogeneity in AD. This approach to delineate pathway-specific PRS can be used to study other complex diseases.

We compared accuracy of hippocampus and basal forebrain cholinergic system (BFCS) atrophy to predict cortical amyloid burden in 179 cognitively normal subjects (CN), 269 subjects with early stages of mild cognitive impairment (MCI), 136 subjects with late stages of MCI, and 86 subjects with Alzheimer's disease (AD) dementia retrieved from the Alzheimer's Disease Neuroimaging Initiative database. Hippocampus and BFCS volumes were determined from structural magnetic resonance imaging scans at 3 Tesla, and cortical amyloid load from AV45 (florbetapir) positron emission tomography scans. In receiver operating characteristics analyses, BFCS volume provided significantly more accurate classification into amyloid-negative and -positive categories than hippocampus volume. In contrast, hippocampus volume more accurately identified the diagnostic categories of AD, late and early MCI, and CN compared with whole and anterior BFCS volume, whereas posterior BFCS and hippocampus volumes yielded similar diagnostic accuracy. In logistic regression analysis, hippocampus and posterior BFCS volumes contributed significantly to discriminate MCI and AD from CN, but only BFCS volume predicted amyloid status. Our findings suggest that BFCS atrophy is more closely associated with cortical amyloid burden than hippocampus atrophy in predementia AD.

β-site amyloid precursor protein-cleaving enzyme 1 (BACE1) plays an important role in the development of Alzheimer's disease (AD), freeing the amyloid-β (Aβ) N-terminus from the amyloid-β protein precursor (AβPP), the first step in Aβ formation. Increased BACE1 activity in AD brain or cerebrospinal fluid (CSF) has been reported. Other studies, however, found either no change or a decrease with AD diagnosis in either BACE1 activity or sAβPPβ, the N-terminal secreted product of BACE1 (sBACE1) activity on AβPP. Here, sBACE1 enzymatic activity and secreted AβPPβ (sAβPPβ) were measured in Alzheimer's Disease Neuroimaging Initiative-1 (ADNI-1) baseline CSF samples and no statistically significant changes were found in either measure comparing healthy control, mild cognitively impaired, or AD individual samples. While CSF sBACE1 activity and sAβPPβ demonstrated a moderate yet significant degree of correlation with each other, there was no correlation of either analyte to CSF Aβ peptide ending at residue 42. Surprisingly, a stronger correlation was demonstrated between CSF sBACE1 activity and tau, which was comparable to that between CSF Aβ₄₂ and tau. Unlike for these latter two analytes, receiver-operator characteristic curves demonstrate that neither CSF sBACE1 activity nor sAβPPβ concentrations can be used to differentiate between healthy elderly and AD individuals.

Patients with Alzheimer's disease vary in their ability to sustain cognitive abilities in the presence of brain pathology. A major open question is which brain mechanisms may support higher reserve capacity, i.e. relatively high cognitive performance at a given level of Alzheimer's pathology. Higher functional MRI-assessed functional connectivity of a hub in the left frontal cortex is a core candidate brain mechanism underlying reserve as it is associated with education (i.e. a protective factor often associated with higher reserve) and attenuated cognitive impairment in prodromal Alzheimer's disease. However, no study has yet assessed whether such hub connectivity of the left frontal cortex supports reserve throughout the evolution of pathological brain changes in Alzheimer's disease, including the presymptomatic stage when cognitive decline is subtle. To address this research gap, we obtained cross-sectional resting state functional MRI in 74 participants with autosomal dominant Alzheimer's disease, 55 controls from the Dominantly Inherited Alzheimer's Network and 75 amyloid-positive elderly participants, as well as 41 amyloid-negative cognitively normal elderly subjects from the German Center of Neurodegenerative Diseases multicentre study on biomarkers in sporadic Alzheimer's disease. For each participant, global left frontal cortex connectivity was computed as the average resting state functional connectivity between the left frontal cortex (seed) and each voxel in the grey matter. As a marker of disease stage, we applied estimated years from symptom onset in autosomal dominantly inherited Alzheimer's disease and cerebrospinal fluid tau levels in sporadic Alzheimer's disease cases. In both autosomal dominant and sporadic Alzheimer's disease patients, higher levels of left frontal cortex connectivity were correlated with greater education. For autosomal dominant Alzheimer's disease, a significant left frontal cortex connectivity × estimated years of onset interaction was found, indicating slower decline of memory and global cognition at higher levels of connectivity. Similarly, in sporadic amyloid-positive elderly subjects, the effect of tau on cognition was attenuated at higher levels of left frontal cortex connectivity. Polynomial regression analysis showed that the trajectory of cognitive decline was shifted towards a later stage of Alzheimer's disease in patients with higher levels of left frontal cortex connectivity. Together, our findings suggest that higher resilience against the development of cognitive impairment throughout the early stages of Alzheimer's disease is at least partially attributable to higher left frontal cortex-hub connectivity.

Predicting progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is clinically important. In this study, we propose a dual-model radiomic analysis with multivariate Cox proportional hazards regression models to investigate promising risk factors associated with MCI conversion to AD. T1 structural magnetic resonance imaging (MRI) and ¹⁸F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) data, from the AD Neuroimaging Initiative database, were collected from 131 patients with MCI who converted to AD within 3 years and 132 patients with MCI without conversion within 3 years. These subjects were randomly partition into 70% training dataset and 30% test dataset with multiple times. We fused MRI and PET images by wavelet method. In a subset of subjects, a group comparison was performed using a two-sample t-test to determine regions of interest (ROIs) associated with MCI conversion. 172 radiomic features from ROIs for each individual were established using a published radiomics tool. Finally, L1-penalized Cox model was constructed and Harrell's C index (C-index) was used to evaluate prediction accuracy of the model. To evaluate the efficacy of our proposed method, we used a same analysis framework to evaluate MRI and PET data separately. We constructed prognostic Cox models with: clinical data, MRI images, PET images, fused MRI/PET images, and clinical variables and fused MRI/PET images in combination. The experimental results showed that captured ROIs significantly associated with conversion to AD, such as gray matter atrophy in the bilateral hippocampus and hypometabolism in the temporoparietal cortex. Imaging model (MRI/PET/fused) provided significant enhancement in prediction of conversion compared to clinical models, especially the fused-modality Cox model. Moreover, the combination of fused-modality imaging and clinical variables resulted in the greatest accuracy of prediction. The average C-index for the clinical/MRI/PET/fused/combined model in the test dataset was 0.69, 0.73, 0.73 and 0.75, and 0.78, respectively. These results suggested that a combination of radiomic analysis and Cox model analyses could be used successfully in survival analysis and may be powerful tools for personalized precision medicine patients with potential to undergo conversion from MCI to AD.