CSF biomarkers of Alzheimer's disease concord with amyloid-β PET and predict clinical progression: A study of fully automated immunoassays in BioFINDER and ADNI cohorts

Estimating Dementia Onset: AT(N) Profiles and Predictive Modeling in Mild Cognitive Impairment Patients

BACKGROUND

Mild Cognitive Impairment (MCI) usually precedes the symptomatic phase of dementia and constitutes a window of opportunities for preventive therapies.

OBJECTIVES

The objective of this study was to predict the time an MCI patient has left to reach dementia and obtain the most likely natural history in the progression of MCI towards dementia.

METHODS

This study was conducted on 633 MCI patients and 145 subjects with dementia through 4726 visits over 15 years from Alzheimer Disease Neuroimaging Initiative (ADNI) cohort. A combination of data from AT(N) profiles at baseline and longitudinal predictive modeling was applied. A data-driven approach was proposed for categorical diagnosis prediction and timeline estimation of cognitive decline progression, which combined supervised and unsupervised learning techniques.

RESULTS

A reduced vector of only neuropsychological measures was selected for training the models. At baseline, this approach had high performance in detecting subjects at high risk of converting from MCI to dementia in the coming years. Furthermore, a Disease Progression Model (DPM) was built and also verified using three metrics. As a result of the DPM focused on the studied population, it was inferred that amyloid pathology (A+) appears about 7 years before dementia, and tau pathology (T+) and neurodegeneration (N+) occur almost simultaneously, between 3 and 4 years before dementia. In addition, MCI-A+ subjects were shown to progress more rapidly to dementia compared to MCI-A- subjects.

CONCLUSION

Based on proposed natural histories and cross-sectional and longitudinal analysis of AD markers, the results indicated that only a single cerebrospinal fluid sample is necessary during the prodromal phase of AD. Prediction from MCI into dementia and its timeline can be achieved exclusively through neuropsychological measures.

Characteristics of very late-onset schizophrenia-like psychosis classified with the biomarkers for Alzheimer's disease: a retrospective cross-sectional study

OBJECTIVES

We aimed to investigate the association between very late-onset schizophrenia-like psychosis (VLOSLP), a schizophrenia spectrum disorder with an onset of ≥60 years, and Alzheimer's disease (AD) using biomarkers.

DESIGN

Retrospective cross-sectional study.

SETTING

Neuropsychology clinic of Osaka University Hospital in Japan.

PARTICIPANTS

Thirty-three participants were classified into three groups: eight AD biomarker-negative VLOSLP (VLOSLP-AD), nine AD biomarker-positive VLOSLP (VLOSLP+AD), and sixteen amnestic mild cognitive impairment due to AD without psychosis (aMCI-P+AD) participants.

MEASUREMENTS

Phosphorylated tau levels in the cerebrospinal fluid and 18F-Florbetapir positron emission tomography results were used as AD biomarkers. Several scales (e.g. the Mini-Mental State Examination (MMSE), Wechsler Memory Scale-Revised (WMS-R) Logical Memory (LM) I and II, and Neuropsychiatric Inventory (NPI)-plus) were conducted to assess clinical characteristics.

RESULTS

Those in both VLOSLP-AD and +AD groups scored higher than those in aMCI-P+AD in WMS-R LM I. On the other hand, VLOSLP+AD participants scored in between the other two groups in the WMS-R LM II, with only VLOSLP-AD participants scoring significantly higher than aMCI-P+AD participants. There were no significant differences in sex distribution and MMSE scores among the three groups or in the subtype of psychotic symptoms between VLOSLP-AD and +AD participants. Four VLOSLP-AD and five VLOSLP+AD participants harbored partition delusions. Delusion of theft was shown in two VLOSLP-AD patients and five VLOSLP+AD patients.

CONCLUSION

Some VLOSLP patients had AD pathology. Clinical characteristics were different between AD biomarker-positive and AD biomarker-negative VLOSLP, which may be helpful for detecting AD pathology in VLOSLP patients.

Associations between recall of proper names in story recall and CSF amyloid and tau in adults without cognitive impairment

Neuropsychological measures sensitive to decline in the preclinical phase of Alzheimer's disease are needed. We previously demonstrated that higher amyloid-beta (Aβ) assessed by positron emission tomography in adults without cognitive impairment was associated with recall of fewer proper names in Logical Memory story recall. The current study investigated the association between proper names and cerebrospinal fluid biomarkers (Aβ42/40, phosphorylated tau181 [pTau181], neurofilament light) in 223 participants from the Wisconsin Registry for Alzheimer's Prevention. We assessed associations between biomarkers and delayed Logical Memory total score and proper names using binary logistic regressions. Sensitivity analyses used multinomial logistic regression and stratified biomarker groups. Lower Logical Memory total score and proper names scores from the most recent visit were associated with biomarker positivity. Relatedly, there was a 27% decreased risk of being classified Aβ42/40+/pTau181+ for each additional proper name recalled. A linear mixed effects model found that longitudinal change in proper names recall was predicted by biomarker status. These results demonstrate a novel relationship between proper names and Alzheimer's disease-cerebrospinal fluid pathology.

Reduced overnight memory consolidation and associated alterations in sleep spindles and slow oscillations in early Alzheimer's disease

Spatial navigation critically underlies hippocampal-entorhinal circuit function that is early affected in Alzheimer's disease (AD). There is growing evidence that AD pathophysiology dynamically interacts with the sleep/wake cycle impairing hippocampal memory. To elucidate sleep-dependent consolidation in a cohort of symptomatic AD patients (n = 12, 71.25 ± 2.16 years), we tested hippocampal place learning by means of a virtual reality task and verbal memory by a word-pair association task before and after a night of sleep. Our results show an impaired overnight memory retention in AD compared with controls in the verbal task, together with a significant reduction of sleep spindle activity (i.e., lower amplitude of fast sleep spindles, p = 0.016) and increased duration of the slow oscillation (SO; p = 0.019). Higher spindle density, faster down-to-upstate transitions within SOs, and the time delay between SOs and nested spindles predicted better memory performance in healthy controls but not in AD patients. Our results show that mnemonic processing and memory consolidation in AD is slightly impaired as reflected by dysfunctional oscillatory dynamics and spindle-SO coupling during NonREM sleep. In this translational study based on experimental paradigms in animals and extending previous work in healthy aging and preclinical disease stages, our results in symptomatic AD further deepen the understanding of the memory decline within a bidirectional relationship of sleep and AD pathology.

A rat model established by simulating genetic-environmental interactions recapitulates human Alzheimer's disease pathology

BACKGROUND

In humans, Alzheimer's disease (AD) is typically sporadic in nature, and its pathology is usually influenced by extensive factors. The study established a rat model based on the genetic-environmental interaction.

METHODS

A rat model was established by transduction of an adeno-associated virus combined with acrolein treatment. Rats were assigned to the normal control (NC), acrolein group, AAV (-) group, AAV-APP group, and AAV-APP/acrolein group. The success of model construction was verified in multiple ways, including by assessing cognitive function, examining microstructural changes in the brain in vivo, and performing immunohistochemistry. The contribution of genetic (APP mutation) and environmental (acrolein) factors to AD-like phenotypes in the model was explored by factorial analysis.

RESULTS

1) The AAV-APP/acrolein group showed a decline in cognitive function, as indicated by a reduced gray matter volume in key cognition-related brain areas, lower FA values in the hippocampus and internal olfactory cortex, and Aβ deposition in the cortex and hippocampus. 2) The AAV-APP group also showed a decline in cognitive function, although the group exhibited atypical brain atrophy in the gray matter and insignificant Aβ deposition. 3) The acrolein group did not show any significant changes in Aβ levels, gray matter volume, or cognitive function. 4) The genetic factor (APP mutation) explained 39.74% of the AD-like phenotypes in the model factors, and the environmental factor (acrolein exposure) explained 33.3%.

CONCLUSIONS

The genetic-environmental interaction rat model exhibited a phenotype that resembled the features of human AD and will be useful for research on AD.

Where Should I Draw the Line: PET-Driven, Data-Driven, or Manufacturer Cut-Off?

Background

The optimal cut-off for Alzheimer's disease (AD) CSF biomarkers remains controversial.

Objective

To analyze the performance of cut-off points standardized by three methods: one that optimized the agreement between 11C-Pittsburgh compound B PET (a-PET) and CSF biomarkers (Aβ1-42, pTau, tTau, and Aβ1-42/Aβ1-40 ratio) in our population, called PET-driven; an unbiased cut-off using data from a healthy research cohort, called data-driven, and that provided by the manufacturer. We also compare changes in ATN classification.

Methods

CSF biomarkers measured by the LUMIPULSE G600II platform and qualitative visualization of amyloid positron emission tomography (a-PET) were performed in all the patients. We established a cut-off for each single biomarker and Aβ1-42/Aβ1-40 ratio that optimized their agreement with a-PET using ROC curves. Sensitivity, Specificity, and Overall Percent of Agreement are assessed using a-PET or clinical diagnosis as gold standard for every cut-off. Also, we established a data-driven cut-off from our cognitively unimpaired cohort. We then analyzed changes in ATN classification.

Results

One hundred and ten patients were recruited. Sixty-six (60%) were a-PET positive. PET-driven cut-offs were: pTau > 57, tTau > 362.62, Aβ1-42/Aβ1-40 < 0.069. For a single biomarker, pTau showed the highest accuracy (AUC 0.926). New PET-driven cut-offs classified patients similarly to manufacturer cut-offs (only two patients changed). However, 20 patients (18%) changed when data-driven cut-offs were used.

Conclusions

We established our sample's best CSF biomarkers cut-offs using a-PET as the gold standard. These cut-offs categorize better symptomatic subjects than data-driven in ATN classification, but they are very similar to the manufacturer's.

Day-to-day sleep variability with Alzheimer's biomarkers in at-risk elderly

INTRODUCTION

Measuring day-to-day sleep variability might reveal unstable sleep-wake cycles reflecting neurodegenerative processes. We evaluated the association between Alzheimer's disease (AD) fluid biomarkers with day-to-day sleep variability.

METHODS

In the PREVENT-AD cohort, 203 dementia-free participants (age: 68.3 ± 5.4; 78 males) with a parental history of sporadic AD were tested with actigraphy and fluid biomarkers. Day-to-day variability (standard deviations over a week) was assessed for sleep midpoint, duration, efficiency, and nighttime activity count.

RESULTS

Lower cerebrospinal fluid (CSF) ApoE, higher CSF p-tau181/amyloid-β (Aβ)42, and higher plasma p-tau231/Aβ42 were associated with higher variability of sleep midpoint, sleep duration, and/or activity count. The associations between fluid biomarkers with greater sleep duration variability were especially observed in those that carried the APOE4 allele, mild cognitive impairment converters, or those with gray matter atrophy.

DISCUSSION

Day-to-day sleep variability were associated with biomarkers of AD in at-risk individuals, suggesting that unstable sleep promotes neurodegeneration or, conversely, that AD neuropathology disrupts sleep-wake cycles.

Risk of cognitive decline progression is associated to increased blood-brain-barrier permeability: A longitudinal study in a memory unit clinical cohort

INTRODUCTION

This study examined the relationship between blood-brain-barrier permeability (BBBp), measured by cerebrospinal fluid/serum albumin ratio (QAlb), and cognitive decline progression in a clinical cohort.

METHODS

This prospective observational study included 334 participants from the BIODEGMAR cohort. Cognitive decline progression was defined as an increase in Global Deterioration Scale and/or Clinical Dementia Rating scores. Associations between BBBp, demographics, and clinical factors were explored.

RESULTS

Male sex, diabetes mellitus, and cerebrovascular burden were associated with increased log-QAlb. Vascular cognitive impairment patients had the highest log-QAlb levels. Among the 273 participants with valid follow-up data, 154 (56.4%) showed cognitive decline progression. An 8% increase in the hazard of clinical worsening was observed for each 10% increase in log-QAlb.

DISCUSSION

These results suggest that increased BBBp in individuals with cognitive decline may contribute to clinical worsening, pointing to potential targeted therapies. QAlb could be a useful biomarker for identifying patients with a worse prognosis.

Optimising Alzheimer's Disease Diagnosis and Treatment: Assessing Cost-Utility of Integrating Blood Biomarkers in Clinical Practice for Disease-Modifying Treatment

BACKGROUND

Recent developments in blood biomarkers (BBM) have shown promising results in diagnosing amyloid pathology in Alzheimer's Disease (AD). However, information on how these BBMs can best be used in clinical settings to optimise clinical decision-making and long-term health outcomes for individuals with AD is still lacking.

OBJECTIVES

We aim to assess the potential value of BBM in AD diagnosis within the context of disease-modifying treatment (DMT).

DESIGN

We developed a decision analytic model to evaluate the long-term health outcomes using BBM in AD diagnosis. We compared standard of care (SOC) diagnosis workflow to the integration of BBM as a (1) referral decision tool in primary health center (PHC) and (2) triaging tool for invasive CSF examination in specialist memory clinic (MC). We combined a decision tree and a Markov model to simulate the patient's diagnostic journey, treatment decisions following diagnosis and long-term health outcomes. Input parameters for the model were identified from published literature and registry data analysis. We conducted a cost-utility analysis from the societal perspective using a one-year cycle length and a 30-year (lifetime) horizon.

MEASUREMENTS

We reported the simulated outcomes in the percentage of correct diagnosis, costs (in 2022 Euros), quality-adjusted life year (QALY), and incremental cost-effectiveness ratios (ICER) associated with each diagnosis strategy.

RESULTS

Compared to SOC, integrating BBM in PHC increased patient referrals by 8% and true positive AD diagnoses by 10.4%. The lifetime costs for individuals diagnosed with AD were € 249,685 and €250,287, and QALYs were 9.5 and 9.52 in SOC and PHC pathways, respectively. The cost increments were €603, and QALYs gained were 0.01, resulting in an ICER of €48,296. Using BBM in MC reduced the exposure to invasive CSF procedures and costs but also reduced true positive AD diagnoses and QALYs.

CONCLUSIONS

Using BBM at PHC to make referral decisions might increase initial diagnostic costs but can prevent high costs associated with disease progression, providing a cost-effective DMT is available, whereas using BBM in MC could reduce the initial evaluation cost but incur high costs associated with disease progression.

Plasma Biomarkers as Predictors of Progression to Dementia in Individuals with Mild Cognitive Impairment

Background

Blood-based biomarkers (BBMs) are of growing interest in the field of Alzheimer's disease (AD) and related dementias.

Objective

This study aimed to assess the ability of plasma biomarkers to 1) predict disease progression from mild cognitive impairment (MCI) to dementia and 2) improve the predictive ability of magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) measures when combined.

Methods

We used data from the Alzheimer's Disease Neuroimaging Initiative. Machine learning models were trained using the data from participants who remained cognitively stable (CN-s) and with Dementia diagnosis at 2-year follow-up visit. The models were used to predict progression to dementia in MCI individuals. We assessed the performance of models with plasma biomarkers against those with CSF and MRI measures, and also in combination with them.

Results

Our models with plasma biomarkers classified CN-s individuals from AD with an AUC of 0.75±0.03 and could predict conversion to dementia in MCI individuals with an AUC of 0.64±0.03 (17.1% BP, base prevalence). Models with plasma biomarkers performed better when combined with CSF and MRI measures (CN versus AD: AUC of 0.89±0.02; MCI-to-AD: AUC of 0.76±0.03, 21.5% BP).

Conclusions

Our results highlight the potential of plasma biomarkers in predicting conversion to dementia in MCI individuals. While plasma biomarkers could improve the predictive ability of CSF and MRI measures when combined, they also show the potential to predict non-progression to AD when considered alone. The predictive ability of plasma biomarkers is crucially linked to reducing the costly and effortful collection of CSF and MRI measures.

Optimized Pre-analytical Handling Protocol for Blood-Based Biomarkers of Alzheimer's Disease

The therapeutic management of patients with Alzheimer's disease (AD) has been hindered by poor diagnostic accuracy. As such, there is an unmet clinical need for tools that can detect and diagnose the disease in its early stages. Compared with cerebrospinal fluid (CSF)-based biomarkers or positron emission tomography (PET), the use of reliable blood-based biomarkers could offer an accessible and minimally invasive method of streamlining diagnosis in the clinical setting. However, the influence of pre-analytical processing and sample handling parameters on the accurate measurement of protein biomarkers is well established, especially for AD CSF-based biomarkers. In this chapter, we provide recommendations for an optimal sample handling protocol for the analysis of blood-based biomarkers specifically for amyloid pathology in AD.

Dynamic Features of Body Mass Index in Late Life Predict Cognitive Trajectories and Alzheimer's Disease: A Longitudinal Study

Background

The causal relationships of late-life body mass index (BMI) with Alzheimer's disease (AD) remains debated.

Objective

We aimed to assess the associations of dynamic BMI features (ΔBMIs) with cognitive trajectories, AD biomarkers, and incident AD risk.

Methods

We analyzed an 8-year cohort of 542 non-demented individuals who were aged ≥65 years at baseline and had BMI measurements over the first 4 years. ΔBMIs were defined as changing extent (change ≤ or >5%), variability (standard deviation), and trajectories over the first 4 years measured using latent class trajectory modeling. Linear mixed-effect models were utilized to examine the influence of ΔBMIs on changing rates of AD pathology biomarkers, hippocampus volume, and cognitive functions. Cox proportional hazards models were used to test the associations with AD risk. Stratified analyzes were conducted by the baseline BMI group and age.

Results

Over the 4-year period, compared to those with stable BMI, individuals who experienced BMI decreases demonstrated accelerated declined memory function (p = 0.006) and amyloid-β deposition (p = 0.034) while BMI increases were associated with accelerated hippocampal atrophy (p = 0.036). Three BMI dynamic features, including stable BMI, low BMI variability, and persistently high BMI, were associated with lower risk of incident AD (p < 0.005). The associations were validated over the 8-year period after excluding incident AD over the first 4 years. No stratified effects were revealed by the BMI group and age.

Conclusions

High and stable BMI in late life could predict better cognitive trajectory and lower risk of AD.

Visual Functions Are Associated with Biomarker Changes in Alzheimer's Disease

Background

While various biomarkers of Alzheimer's disease (AD) have been associated with general cognitive function, their association to visual-perceptive function across the AD spectrum warrant more attention due to its significant impact on quality of life. Thus, this study explores how AD biomarkers are associated with decline in this cognitive domain.

Objective

To explore associations between various fluid and imaging biomarkers and visual-based cognitive assessments in participants across the AD spectrum.

Methods

Data from participants (N = 1,460) in the Alzheimer's Disease Neuroimaging Initiative were analyzed, including fluid and imaging biomarkers. Along with the Mini-Mental State Examination (MMSE), three specific visual-based cognitive tests were investigated: Trail Making Test (TMT) A and TMT B, and the Boston Naming Test (BNT). Locally estimated scatterplot smoothing curves and Pearson correlation coefficients were used to examine associations.

Results

MMSE showed the strongest correlations with most biomarkers, followed by TMT-B. The p-tau181/Aβ1-42 ratio, along with the volume of the hippocampus and entorhinal cortex, had the strongest associations among the biomarkers.

Conclusions

Several biomarkers are associated with visual processing across the disease spectrum, emphasizing their potential in assessing disease severity and contributing to progression models of visual function and cognition.

The relationship between APOE genotype, CSF Tau and cognition across the Alzheimer's disease spectrum, moderation and mediation role of insula network connectivity

AIMS

To investigate whether insula network connectivity modulates the relationship between apolipoprotein E (APOE) ε4 genotype, cerebrospinal fluid (CSF) biomarkers (Aβ, Tau, and pTau) and cognition across Alzheimer's disease (AD) spectrum.

METHODS

Forty-six cognitive normal (CN), 35 subjective memory complaint (SMC), 41 mild cognitive impairment (MCI), and 32 AD subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were obtained. Multivariable linear regression analyses were conducted to investigate the main effects and interaction of the APOE genotype and disease status on the insula functional connectivity (IFC) network. Mediation and moderation analysis were performed to investigate whether IFC strengths regulate the association between APOE genotype, CSF biomarkers and cognition. Additionally, the support vector machine (SVM) model integrating APOE genotype, CSF biomarkers, and neuroimaging biomarkers (insula volumes and altered regional IFCs) was used to classify the AD spectrum.

RESULTS

The interactive effect of the APOE genotype and disease on the insula network was found in the left medial superior frontal gyrus (SFGmed.L), right anterior medial prefrontal cortex (aMPFC.R), and bilateral thalamus (THA.B). The functional connectivities (FCs) in the left insula (LIns) connecting with the left posterior middle temporal gyrus (pMTG.L), SFGmed.L, and right lingual gyrus (LING.R) were correlated with cognition. LIns-SFGmed.L and LIns-pMTG.L FCs could moderate the effects of Tau on cognition. Furthermore, LIns-SFGmed.L FC may suppress the association between APOE genotype and cognition. More importantly, the integrated biomarkers from the SVM model yielded strong powers for classifying the AD spectrum.

CONCLUSIONS

Insula functional connectivity regulated the association between APOE genotype, CSF Tau and cognition and provided stage-dependent biomarkers for early differentiation of the AD spectrum. The present study used a cross-sectional design. Follow-up studies are needed to validate the relationship.

Molecular Gene Expression Testing to Identify Alzheimer's Disease with High Accuracy from Fingerstick Blood

Background

There is no molecular test for Alzheimer's disease (AD) using self-collected samples, nor is there a definitive molecular test for AD. We demonstrate an accurate and potentially definitive TempO-Seq® gene expression test for AD using fingerstick blood spotted and dried on filter paper, a sample that can be collected in any doctor's office or can be self-collected.

Objective

Demonstrate the feasibility of developing an accurate test for the classification of persons with AD from a minimally invasive sample of fingerstick blood spotted on filter paper which can be obtained in any doctor's office or self-collected to address health disparities.

Methods

Fingerstick blood samples from patients clinically diagnosed with AD, Parkinson's disease (PD), or asymptomatic controls were spotted onto filter paper in the doctor's office, dried, and shipped to BioSpyder for testing. Three independent patient cohorts were used for training/retraining and testing/retesting AD and PD classification algorithms.

Results

After initially identifying a 770 gene classification signature, a minimum set of 68 genes was identified providing classification test areas under the ROC curve of 0.9 for classifying patients as having AD, and 0.94 for classifying patients as having PD.

Conclusions

These data demonstrate the potential to develop a screening and/or definitive, minimally invasive, molecular diagnostic test for AD and PD using dried fingerstick blood spot samples that are collected in a doctor's office or clinic, or self-collected, and thus, can address health disparities. Whether the test can classify patients with AD earlier then possible with cognitive testing remains to be determined.

Mapping the human brain proteome: opportunities, challenges, and clinical potential

INTRODUCTION

Due to the segmented functions and complexity of the human brain, the characterization of molecular profiles within specific areas such as brain structures and biofluids is essential to unveil the molecular basis for structure specialization as well as the molecular imbalance associated with neurodegenerative and psychiatric diseases.

AREAS COVERED

Much of our knowledge about brain functionality derives from neurophysiological, anatomical, and transcriptomic approaches. More recently, laser capture and imaging proteomics, technological and computational developments in LC-MS/MS, as well as antibody/aptamer-based platforms have allowed the generation of novel cellular, spatial, and posttranslational dimensions as well as innovative facets in biomarker validation and druggable target identification.

EXPERT OPINION

Proteomics is a powerful toolbox to functionally characterize, quantify, and localize the extensive protein catalog of the human brain across physiological and pathological states. Brain function depends on multi-dimensional protein homeostasis, and its elucidation will help us to characterize biological pathways that are essential to properly maintain cognitive functions. In addition, comprehensive human brain pathological proteomes may be the basis in computational drug-repositioning methods as a strategy for unveiling potential new therapies in neurodegenerative and psychiatric disorders.

Cognition Mediates the Association Between Cerebrospinal Fluid Biomarkers of Amyloid and P-Tau and Neuropsychiatric Symptoms

Background

Neuropsychiatric symptoms (NPS) can be an early manifestation of Alzheimer's disease (AD). However, the associations among NPS, cognition, and AD biomarkers across the disease spectrum are unclear.

Objective

We analyzed cross-sectional mediation pathways between cerebrospinal fluid (CSF) biomarkers of AD (Aβ1-42, p-tau181), cognitive function, and NPS.

Methods

Primary models included 781 participants from the National Alzheimer's Coordinating Center (NACC) data set who had CSF analyzed for AD biomarkers using Lumipulse. NPS were assessed with the Neuropsychiatric Inventory Questionnaire (NPI-Q). We assessed cognition with the harmonized MMSE/MoCA, as well as neuropsychological tests sensitive to AD pathology: story recall, naming, animal fluency, and Trails B. The Clinical Dementia Rating (CDR®) scale assessed dementia severity. Mediation models were estimated with Kemeny metric covariance in a structural equation model framework, controlling for age, education, sex, and APOEɛ4.

Results

The sample was older adults (M = 73.85, SD = 6.68; 49.9% male, 390; 27.9% dementia, 218) who were predominantly white (n = 688, 88.1%). Higher p-tau181/Aβ1-42 ratio predicted higher NPI-Q, which was partially mediated by the MMSE/MoCA and, in a second model, story recall. No other pathway was statistically significant. Both the MMSE/MoCA and NPI-Q independently mediated the association between p-tau181/Aβ1-42 ratio and CDR global impairment. With dementia excluded, p-tau181/Aβ1-42 ratio was no longer associated with the NPI-Q.

Conclusions

NPS may be secondary to cognitive impairment and AD pathology through direct and indirect pathways. NPS independently predict dementia severity in AD. However, AD pathology likely plays less of a role in NPS in samples without dementia.

CSF Extracellular Vesicle Aβ42 and Tau/Aβ42 Ratio Are Associated with Cognitive Impairment in Older People with HIV

HIV-associated neurocognitive disorders (HAND) remain prevalent despite viral suppression on antiretroviral therapy (ART). Older people with HIV (PWH) are also at risk for amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD). β-amyloid (Aβ) and Tau biomarkers are associated with aMCI/AD, but their relationship to HAND is unclear. Given the role of extracellular vesicles (EVs) in age-related neurological disorders, we investigated soluble and EV-associated Aβ42, total Tau, NFL, GFAP, ICAM-1, VCAM-1, and CRP in relation to cognitive impairment in PWH. Plasma and CSF EVs were isolated from 184 participants (98 PWH on ART and 86 HIV- controls). Biomarkers were measured using Meso Scale Discovery assays. The median age of PWH was 53 years, and 52% were diagnosed with mild forms of HAND. PWH had increased plasma NFL (p = 0.04) and CSF Aβ42 (p = 0.0003) compared with HIV- controls but no significant difference in Tau or EV-associated forms of these markers. CSF EV Aβ42 was decreased (p = 0.0002) and CSF EV Tau/Aβ42 ratio was increased (p = 0.001) in PWH with HAND vs. no HAND, while soluble forms of these markers showed no significant differences. Decreased CSF EV Aβ42 (p < 0.0001) and an increased CSF EV Tau/Aβ42 ratio (p = 0.0003) were associated with lower neurocognitive T scores in age-adjusted models; an optimal model included both CSF EV Aβ42 and plasma NFL. Levels of soluble, but not EV-associated, ICAM-1, VCAM-1, and CRP were increased in PWH with HAND vs. no HAND (p < 0.05). These findings suggest that decreased Aβ42 and an increased Tau/Aβ42 ratio in CSF EVs are associated with cognitive impairment in older PWH, and these EV-associated biomarkers may help to distinguish aMCI/AD from HIV-related cognitive disorders in future studies.

Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures

Brain aging is a complex process influenced by various lifestyle, environmental, and genetic factors, as well as by age-related and often co-existing pathologies. MRI and, more recently, AI methods have been instrumental in understanding the neuroanatomical changes that occur during aging in large and diverse populations. However, the multiplicity and mutual overlap of both pathologic processes and affected brain regions make it difficult to precisely characterize the underlying neurodegenerative profile of an individual from an MRI scan. Herein, we leverage a state-of-the art deep representation learning method, Surreal-GAN, and present both methodological advances and extensive experimental results that allow us to elucidate the heterogeneity of brain aging in a large and diverse cohort of 49,482 individuals from 11 studies. Five dominant patterns of neurodegeneration were identified and quantified for each individual by their respective (herein referred to as) R-indices. Significant associations between R-indices and distinct biomedical, lifestyle, and genetic factors provide insights into the etiology of observed variances. Furthermore, baseline R-indices showed predictive value for disease progression and mortality. These five R-indices contribute to MRI-based precision diagnostics, prognostication, and may inform stratification into clinical trials.

Protocol for the Tallaght University Hospital Institute for Memory and Cognition-Biobank for Research in Ageing and Neurodegeneration

INTRODUCTION

Alzheimer's disease and other dementias affect >50 million individuals globally and are characterised by broad clinical and biological heterogeneity. Cohort and biobank studies have played a critical role in advancing the understanding of disease pathophysiology and in identifying novel diagnostic and treatment approaches. However, further discovery and validation cohorts are required to clarify the real-world utility of new biomarkers, facilitate research into the development of novel therapies and advance our understanding of the clinical heterogeneity and pathobiology of neurodegenerative diseases.

METHODS AND ANALYSIS

The Tallaght University Hospital Institute for Memory and Cognition Biobank for Research in Ageing and Neurodegeneration (TIMC-BRAiN) will recruit 1000 individuals over 5 years. Participants, who are undergoing diagnostic workup in the TIMC Memory Assessment and Support Service (TIMC-MASS), will opt to donate clinical data and biological samples to a biobank. All participants will complete a detailed clinical, neuropsychological and dementia severity assessment (including Addenbrooke's Cognitive Assessment, Repeatable Battery for Assessment of Neuropsychological Status, Clinical Dementia Rating Scale). Participants undergoing venepuncture/lumbar puncture as part of the clinical workup will be offered the opportunity to donate additional blood (serum/plasma/whole blood) and cerebrospinal fluid samples for longitudinal storage in the TIMC-BRAiN biobank. Participants are followed at 18-month intervals for repeat clinical and cognitive assessments. Anonymised clinical data and biological samples will be stored securely in a central repository and used to facilitate future studies concerned with advancing the diagnosis and treatment of neurodegenerative diseases.

ETHICS AND DISSEMINATION

Ethical approval has been granted by the St. James's Hospital/Tallaght University Hospital Joint Research Ethics Committee (Project ID: 2159), which operates in compliance with the European Communities (Clinical Trials on Medicinal Products for Human Use) Regulations 2004 and ICH Good Clinical Practice Guidelines. Findings using TIMC-BRAiN will be published in a timely and open-access fashion.

Detection of APP gene recombinant in human blood plasma

The pathogenesis of Alzheimer's disease (AD) is believed to involve the accumulation of amyloid-β in the brain, which is produced by the sequential cleavage of amyloid precursor protein (APP) by β-secretase and γ-secretase. Recently, analysis of genomic DNA and mRNA from postmortem brain neurons has revealed intra-exonic recombinants of APP (gencDNA), which have been implicated in the accumulation of amyloid-β. In this study, we computationally analyzed publicly available sequence data (SRA) using probe sequences we constructed to screen APP gencDNAs. APP gencDNAs were detected in SRAs constructed from both genomic DNA and RNA obtained from the postmortem brain and in the SRA constructed from plasma cell-free mRNA (cf-mRNA). The SRA constructed from plasma cf-mRNA showed a significant difference in the number of APP gencDNA reads between SAD and NCI: the p-value from the Mann-Whitney U test was 5.14 × 10-6. The transcripts were also found in circulating nucleic acids (CNA) from our plasma samples with NGS analysis. These data indicate that transcripts of APP gencDNA can be detected in blood plasma and suggest the possibility of using them as blood biomarkers for Alzheimer's disease.

Association Between Serum Neurofilament Light Chain and Cognitive Performance Among Older Adults in the United States: A Cross-Sectional Study

INTRODUCTION

Serum neurofilament light chain (sNfL) is an emerging biomarker of neuronal damage in several neurological disorders. Its association with cognitive function in the general US population aged 60 years and above is unknown. The aim of this study was to investigate the correlation between sNfL and cognitive function in the general US population aged 60 and above.

METHODS

The data were obtained from the 2013-2014 National Health and Nutrition Examination Survey (NHANES), which include 506 individuals aged 60 or older who met our search criteria. In our study, sNfL levels were divided into two groups based on dichotomization (19.0 pg/mL). After adjusting for multiple covariates, it was found that the high sNfL group (≥ 19.0 pg/mL) had lower cognitive performance than the low sNfL group (< 19.0 pg/mL). This relationship was also stable in subgroup analysis.

CONCLUSION

In this sample of an American elderly population, higher sNfL levels are correlated with lower cognitive performance. Our findings suggest that sNfL may become a potential screening tool for early prediction and confirmation of cognitive damage.

The Biochemistry Behind Cognitive Decline: Biomarkers of Alzheimer's Disease

Alzheimer's disease (AD) is the most prevalent type of dementia. Pathologically, the disease is marked by neurofibrillary tangles (NFT), which are aberrant accumulations of the tau protein that develop inside neurons, and extracellular plaque deposits of the amyloid β peptide (Aβ). These pathological lesions are present in the brain before the beginning of clinical manifestations. However, despite advancements in the comprehension of AD pathophysiology, timely and accurate clinical diagnosis remains challenging. Therefore, developing biomarkers capable of detecting AD during the preclinical phase holds enormous promise for precise diagnosis since detecting the disease early is crucial because it enables interventions when treatments may be more effective. This article intends to provide a comprehensive review of AD biomarkers, discussing their significance, classification, and recent developments in the field.

Association of APOE-ε4 and GAP-43-related presynaptic loss with β-amyloid, tau, neurodegeneration, and cognitive decline

Apolipoprotein E-ε4 (APOE-ε4) carriers had elevated cerebrospinal fluid (CSF) presynaptic protein growth-associated protein-43 (GAP-43), but the underlying mechanism is not fully understood. We investigated how the APOE-ε4 genotype affects the baseline and longitudinal changes in CSF GAP-43 and their associations with β-amyloid positron emission tomography (Aβ PET), CSF phosphorylated tau 181 (p-Tau181), neurodegeneration, and cognitive decline. Compared to APOE-ε4 non-carriers, APOE-ε4 carriers had higher baseline levels and faster rates of increases in Aβ PET, CSF p-Tau181, and CSF GAP-43. Both higher baseline levels and faster rates of increase in CSF GAP-43 were associated with greater baseline Aβ PET and CSF p-Tau181, which fully mediated the APOE-ε4 effect on CSF GAP-43 elevations. Independent of Aβ PET and CSF p-Tau181, APOE-ε4 carriage was associated with exacerbated GAP-43-related longitudinal hippocampal atrophy and cognitive decline, especially in Aβ+ participants (GAP-43 × time × APOE-ε4). These findings suggest that the APOE-ε4 effect on GAP-43-related presynaptic dysfunction is mediated by primary Alzheimer's pathologies and independently correlates to hippocampal atrophy and cognitive decline in the future.

CSF 14-3-3β is associated with progressive cognitive decline in Alzheimer's disease

Alzheimer's disease is a neurodegenerative disorder characterized pathologically by amyloid-beta plaques, tau tangles and neuronal loss. In clinical practice, the 14-3-3 isoform beta (β) is a biomarker that aids in the diagnosis of sporadic Creutzfeldt-Jakob disease. Recently, a proteomics study found increased CSF 14-3-3β levels in Alzheimer's disease patients, suggesting a potential link between CSF 14-3-3β and Alzheimer's disease. Our present study aimed to further investigate the role of CSF 14-3-3β in Alzheimer's disease by analysing the data of 719 participants with available CSF 14-3-3β measurements from the Alzheimer's Disease Neuroimaging Initiative. Higher CSF 14-3-3β levels were observed in the mild cognitive impairment group compared to the cognitively normal group, with the highest CSF 14-3-3β levels in the Alzheimer's disease dementia group. This study also found significant associations between CSF 14-3-3β levels and CSF biomarkers of p-tau, t-tau, pTau/Aβ42 ratios and GAP-43, as well as other Alzheimer's disease biomarkers such as Aβ-PET. An early increase in CSF 14-3-3β levels was observed prior to Aβ-PET-positive status, and CSF 14-3-3β levels continued to rise after crossing the Aβ-PET positivity threshold before reaching a plateau. The diagnostic accuracy of CSF 14-3-3β (area under the receiver operating characteristic curve = 0.819) was moderate compared to other established Alzheimer's disease biomarkers in distinguishing cognitively normal Aβ pathology-negative individuals from Alzheimer's disease Aβ pathology-positive individuals. Higher baseline CSF 14-3-3β levels were associated with accelerated cognitive decline, reduced hippocampus volumes and declining fluorodeoxyglucose-PET values over a 4-year follow-up period. Patients with mild cognitive impairment and high CSF 14-3-3β levels at baseline had a significantly increased risk [hazard ratio = 2.894 (1.599-5.238), P < 0.001] of progression to Alzheimer's disease dementia during follow-up. These findings indicate that CSF 14-3-3β may be a potential biomarker for Alzheimer's disease and could provide a more comprehensive understanding of the underlying pathological changes of Alzheimer's disease, as well as aid in the diagnosis and monitoring of disease progression.

A limbic-predominant amnestic neurodegenerative syndrome associated with TDP-43 pathology

Limbic-predominant age-related TDP-43 encephalopathy (LATE) is a neuropathologically-defined disease that affects 40% of persons in advanced age, but its associated neurological syndrome is not defined. LATE neuropathological changes (LATE-NC) are frequently comorbid with Alzheimer's disease neuropathologic changes (ADNC). When seen in isolation, LATE-NC have been associated with a predominantly amnestic profile and slow clinical progression. We propose a set of clinical criteria for a limbic-predominant amnestic neurodegenerative syndrome (LANS) that is highly associated with LATE-NC but also other pathologic entities. The LANS criteria incorporate core, standard and advanced features that are measurable in vivo, including older age at evaluation, mild clinical syndrome, disproportionate hippocampal atrophy, impaired semantic memory, limbic hypometabolism, absence of neocortical degenerative patterns and low likelihood of neocortical tau, with degrees of certainty (highest, high, moderate, low). We operationalized this set of criteria using clinical, imaging and biomarker data to validate its associations with clinical and pathologic outcomes. We screened autopsied patients from Mayo Clinic (n = 922) and ADNI (n = 93) cohorts and applied the LANS criteria to those with an antemortem predominant amnestic syndrome (Mayo, n = 165; ADNI, n = 53). ADNC, ADNC/LATE-NC and LATE-NC accounted for 35%, 37% and 4% of cases in the Mayo cohort, respectively, and 30%, 22%, and 9% of cases in the ADNI cohort, respectively. The LANS criteria effectively categorized these cases, with ADNC having the lowest LANS likelihoods, LATE-NC patients having the highest likelihoods, and ADNC/LATE-NC patients having intermediate likelihoods. A logistic regression model using the LANS features as predictors of LATE-NC achieved a balanced accuracy of 74.6% in the Mayo cohort, and out-of-sample predictions in the ADNI cohort achieved a balanced accuracy of 73.3%. Patients with high LANS likelihoods had a milder and slower clinical course and more severe temporo-limbic degeneration compared to those with low likelihoods. Stratifying ADNC/LATE-NC patients from the Mayo cohort according to their LANS likelihood revealed that those with higher likelihoods had more temporo-limbic degeneration and a slower rate of cognitive decline, and those with lower likelihoods had more lateral temporo-parietal degeneration and a faster rate of cognitive decline. The implementation of LANS criteria has implications to disambiguate the different driving etiologies of progressive amnestic presentations in older age and guide prognosis, treatment, and clinical trials. The development of in vivo biomarkers specific to TDP-43 pathology are needed to refine molecular associations between LANS and LATE-NC and precise antemortem diagnoses of LATE.

Alzheimer Disease Biomarkers: Moving from CSF to Plasma for Reliable Detection of Amyloid and tau Pathology

BACKGROUND

Development of validated biomarkers to detect early Alzheimer disease (AD) neuropathology is needed for therapeutic AD trials. Abnormal concentrations of "core" AD biomarkers, cerebrospinal fluid (CSF) amyloid beta1-42, total tau, and phosphorylated tau correlate well with neuroimaging biomarkers and autopsy findings. Nevertheless, given the limitations of established CSF and neuroimaging biomarkers, accelerated development of blood-based AD biomarkers is underway.

CONTENT

Here we describe the clinical significance of CSF and plasma AD biomarkers to detect disease pathology throughout the Alzheimer continuum and correlate with imaging biomarkers. Use of the AT(N) classification by CSF and imaging biomarkers provides a more objective biologically based diagnosis of AD than clinical diagnosis alone. Significant progress in measuring CSF AD biomarkers using extensively validated highly automated assay systems has facilitated their transition from research use only to approved in vitro diagnostics tests for clinical use. We summarize development of plasma AD biomarkers as screening tools for enrollment and monitoring participants in therapeutic trials and ultimately in clinical care. Finally, we discuss the challenges for AD biomarkers use in clinical trials and precision medicine, emphasizing the possible ethnocultural differences in the levels of AD biomarkers.

SUMMARY

CSF AD biomarker measurements using fully automated analytical platforms is possible. Building on this experience, validated blood-based biomarker tests are being implemented on highly automated immunoassay and mass spectrometry platforms. The progress made developing analytically and clinically validated plasma AD biomarkers within the AT(N) classification scheme can accelerate use of AD biomarkers in therapeutic trials and routine clinical practice.

Hippocampal metabolic subregions and networks: Behavioral, molecular, and pathological aging profiles

INTRODUCTION

Hippocampal local and network dysfunction is the hallmark of Alzheimer's disease (AD).

METHODS

We characterized the spatial patterns of hippocampus differentiation based on brain co-metabolism in healthy elderly participants and demonstrated their relevance to study local metabolic changes and associated dysfunction in pathological aging.

RESULTS

The hippocampus can be differentiated into anterior/posterior and dorsal cornu ammonis (CA)/ventral (subiculum) subregions. While anterior/posterior CA show co-metabolism with different regions of the subcortical limbic networks, the anterior/posterior subiculum are parts of cortical networks supporting object-centered memory and higher cognitive demands, respectively. Both networks show relationships with the spatial patterns of gene expression pertaining to cell energy metabolism and AD's process. Finally, while local metabolism is generally lower in posterior regions, the anterior-posterior imbalance is maximal in late mild cognitive impairment with the anterior subiculum being relatively preserved.

DISCUSSION

Future studies should consider bidimensional hippocampal differentiation and in particular the posterior subicular region to better understand pathological aging.

Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer's disease

Mild cognitive impairment (MCI) is often considered an early stage of dementia, with estimated rates of progression to dementia up to 80-90% after approximately 6 years from the initial diagnosis. Diagnosis of cognitive impairment in dementia is typically based on clinical evaluation, neuropsychological assessments, cerebrospinal fluid (CSF) biomarkers, and neuroimaging. The main goal of diagnosing MCI is to determine its cause, particularly whether it is due to Alzheimer's disease (AD). However, only a limited percentage of the population has access to etiological confirmation, which has led to the emergence of peripheral fluid biomarkers as a diagnostic tool for dementias, including MCI due to AD. Recent advances in biofluid assays have enabled the use of sophisticated statistical models and multimodal machine learning (ML) algorithms for the diagnosis of MCI based on fluid biomarkers from CSF, peripheral blood, and saliva, among others. This approach has shown promise for identifying specific causes of MCI, including AD. After a PRISMA analysis, 29 articles revealed a trend towards using multimodal algorithms that incorporate additional biomarkers such as neuroimaging, neuropsychological tests, and genetic information. Particularly, neuroimaging is commonly used in conjunction with fluid biomarkers for both cross-sectional and longitudinal studies. Our systematic review suggests that cost-effective longitudinal multimodal monitoring data, representative of diverse cultural populations and utilizing white-box ML algorithms, could be a valuable contribution to the development of diagnostic models for AD due to MCI. Clinical assessment and biomarkers, together with ML techniques, could prove pivotal in improving diagnostic tools for MCI due to AD.

Biomarkers in Alzheimer's disease: role in early and differential diagnosis and recognition of atypical variants

BACKGROUND

Development of in vivo biomarkers has shifted the diagnosis of Alzheimer's disease (AD) from the later dementia stages of disease towards the earlier stages and has introduced the potential for pre-symptomatic diagnosis. The International Working Group recommends that AD diagnosis is restricted in the clinical setting to people with specific AD phenotypes and supportive biomarker findings.

MAIN BODY

In this review, we discuss the phenotypic presentation and use of biomarkers for the early diagnosis of typical and atypical AD and describe how this can support clinical decision making, benefit patient communication, and improve the patient journey. Early diagnosis is essential to optimize the benefits of available and emerging treatments. As atypical presentations of AD often mimic other dementias, differential diagnosis can be challenging and can be facilitated using AD biomarkers. However, AD biomarkers alone are not sufficient to confidently diagnose AD or predict disease progression and should be supplementary to clinical assessment to help inform the diagnosis of AD.

CONCLUSIONS

Use of AD biomarkers with incorporation of atypical AD phenotypes into diagnostic criteria will allow earlier diagnosis of patients with atypical clinical presentations that otherwise would have been misdiagnosed and treated inappropriately. Early diagnosis is essential to guide informed discussion, appropriate care and support, and individualized treatment. It is hoped that disease-modifying treatments will impact the underlying AD pathology; thus, determining the patient's AD phenotype will be a critical factor in guiding the therapeutic approach and the assessment of the effects of interventions.

Basal forebrain activity predicts functional degeneration in the entorhinal cortex in Alzheimer's disease

Recent models of Alzheimer's disease suggest the nucleus basalis of Meynert (NbM) as an early origin of structural degeneration followed by the entorhinal cortex (EC). However, the functional properties of NbM and EC regarding amyloid-β and hyperphosphorylated tau remain unclear. We analysed resting-state functional fMRI data with CSF assays from the Alzheimer's Disease Neuroimaging Initiative (n = 71) at baseline and 2 years later. At baseline, local activity, as quantified by fractional amplitude of low-frequency fluctuations, differentiated between normal and abnormal CSF groups in the NbM but not EC. Further, NbM activity linearly decreased as a function of CSF ratio, resembling the disease status. Finally, NbM activity predicted the annual percentage signal change in EC, but not the reverse, independent from CSF ratio. Our findings give novel insights into the pathogenesis of Alzheimer's disease by showing that local activity in NbM is affected by proteinopathology and predicts functional degeneration within the EC.

Association between CSF biomarkers of Alzheimer's disease and neuropsychiatric symptoms: Mayo Clinic Study of Aging

INTRODUCTION

We examined the association between cerebrospinal fluid (CSF)-derived biomarkers of Alzheimer's disease and neuropsychiatric symptoms (NPS) in older non-demented adults.

METHODS

We included 784 persons (699 cognitively unimpaired, 85 with mild cognitive impairment) aged ≥ 50 years who underwent CSF amyloid beta (Aβ42), hyperphosphorylated tau 181 (p-tau), and total tau (t-tau) as well as NPS assessment using Beck Depression and Anxiety Inventories (BDI-II, BAI), and Neuropsychiatric Inventory Questionnaire (NPI-Q).

RESULTS

Lower CSF Aβ42, and higher t-tau/Aβ42 and p-tau/Aβ42 ratios were associated with BDI-II and BAI total scores, clinical depression (BDI-II ≥ 13), and clinical anxiety (BAI ≥ 10), as well as NPI-Q-assessed anxiety, apathy, and nighttime behavior.

DISCUSSION

CSF Aβ42, t-tau/Aβ42, and p-tau/Aβ42 ratios were associated with NPS in community-dwelling individuals free of dementia. If confirmed by a longitudinal cohort study, the findings have clinical relevance of taking into account the NPS status of individuals with abnormal CSF biomarkers.

Recent diagnostic techniques for the detection of Alzheimer's disease: a short review

Alzheimer's disease (AD) is a neurological condition that affects millions of individuals around the world and for which there are few effective therapies. Dementia is characterized by the formation of senile plaques and neurofibrillary tangles, which is followed by neurotoxicity, which results in memory loss and mortality. Pathogenesis occurs several years before the onset of disease. As the disease-modifying drugs are most effective in the early stages of Alzheimer's disease, biomarkers for early detection of disease and their development are crucial. This review discusses the diagnostic utility, benefits, and limitations of traditional techniques such as neuroimaging, cognitive testing, positron emission tomography, and biomarkers, as well as the novel techniques such as artificial intelligence, machine learning, immunotherapy, and blood test approaches for early detection, understanding, and treatment of AD.

Identification of an APOE ε4-specific blood-based molecular pathway for Alzheimer's disease risk

INTRODUCTION

The precise apolipoprotein E (APOE) ε4-specific molecular pathway(s) for Alzheimer's disease (AD) risk are unclear.

METHODS

Plasma protein modules/cascades were analyzed using weighted gene co-expression network analysis (WGCNA) in the Alzheimer's Disease Neuroimaging Initiative study. Multivariable regression analyses were used to examine the associations among protein modules, AD diagnoses, cerebrospinal fluid (CSF) phosphorylated tau (p-tau), and brain glucose metabolism, stratified by APOE genotype.

RESULTS

The Green Module was associated with AD diagnosis in APOE ε4 homozygotes. Three proteins from this module, C-reactive protein (CRP), complement C3, and complement factor H (CFH), had dose-dependent associations with CSF p-tau and cognitive impairment only in APOE ε4 homozygotes. The link among these three proteins and glucose hypometabolism was observed in brain regions of the default mode network (DMN) in APOE ε4 homozygotes. A Framingham Heart Study validation study supported the findings for AD.

DISCUSSION

The study identifies the APOE ε4-specific CRP-C3-CFH inflammation pathway for AD, suggesting potential drug targets for the disease.Highlights: Identification of an APOE ε4 specific molecular pathway involving blood CRP, C3, and CFH for the risk of AD.CRP, C3, and CFH had dose-dependent associations with CSF p-Tau and brain glucose hypometabolism as well as with cognitive impairment only in APOE ε4 homozygotes.Targeting CRP, C3, and CFH may be protective and therapeutic for AD onset in APOE ε4 carriers.

Longitudinal changes in Alzheimer's-related plasma biomarkers and brain amyloid

INTRODUCTION

Understanding longitudinal plasma biomarker trajectories relative to brain amyloid changes can help devise Alzheimer's progression assessment strategies.

METHODS

We examined the temporal order of changes in plasma amyloid-β ratio (A β 42 / A β 40 ${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$ ), glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), and phosphorylated tau ratios (p-tau181 / A β 42 $\text{p-tau181}/\mathrm{A}{\beta}_{42}$ ,p-tau231 / A β 42 $\text{p-tau231}/\mathrm{A}{\beta}_{42}$ ) relative to 11 C-Pittsburgh compound B (PiB) positron emission tomography (PET) cortical amyloid burden (PiB-/+). Participants (n = 199) were cognitively normal at index visit with a median 6.1-year follow-up.

RESULTS

PiB groups exhibited different rates of longitudinal change inA β 42 / A β 40 ( β = 5.41 × 10 - 4 , SE = 1.95 × 10 - 4 , p = 0.0073 ) ${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}\ ( {\beta \ = \ 5.41 \times {{10}}^{ - 4},{\rm{\ SE\ }} = \ 1.95 \times {{10}}^{ - 4},\ p\ = \ 0.0073} )$ . Change in brain amyloid correlated with change in GFAP (r = 0.5, 95% CI = [0.26, 0.68]). The greatest relative decline inA β 42 / A β 40 ${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$ (-1%/year) preceded brain amyloid positivity by 41 years (95% CI = [32, 53]).

DISCUSSION

PlasmaA β 42 / A β 40 ${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$ may begin declining decades prior to brain amyloid accumulation, whereas p-tau ratios, GFAP, and NfL increase closer in time. HIGHLIGHTS PlasmaA β 42 / A β 40 ${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$ declines over time among PiB- but does not change among PiB+. Phosphorylated-tau to Aβ42 ratios increase over time among PiB+ but do not change among PiB-. Rate of change in brain amyloid is correlated with change in GFAP and neurofilament light chain. The greatest decline inA β 42 / A β 40 ${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$ may precede brain amyloid positivity by decades.

A trichotomy method for defining homogeneous subgroups in a dementia population

INTRODUCTION

Diagnosis of dementia in the aging brain is confounded by the presence of multiple pathologies. Mixed dementia (MX), a combination of Alzheimer's disease (AD) proteins with vascular disease (VD), is frequently found at autopsy, and has been difficult to diagnose during life. This report develops a method for separating the MX group and defining preclinical AD (presence of AD factors with normal cognition) and preclinical VD subgroups (presence of white matter damage with normal cognition).

METHODS

Clustering was based on three diagnostic axes: (1) AD factor (ADF) derived from cerebrospinal fluid proteins (Aβ42 and pTau), (2) VD factor (VDF) calculated from mean free water and peak width of skeletonized mean diffusivity in the white matter, and (3) Cognition (Cog) based on memory and executive function. The trichotomy method was applied to an Alzheimer's Disease Neuroimaging Initiative cohort (N = 538).

RESULTS

Eight biologically defined subgroups were identified which included the MX group with both high ADF and VDF (9.3%) and a preclinical VD group (3.9%), and a preclinical AD group (13.6%). Cog is significantly associated with both ADF and VDF, and the partial-correlation remains significant even when the effect of the other variable is removed (r(Cog, ADF/VDF removed) = 0.46, p < 10-28 and r(Cog, VDF/ADF removed) = 0.24, p < 10-7 ).

DISCUSSION

The trichotomy method creates eight biologically characterized patient groups, which includes MX, preclinical AD, and preclinical VD subgroups. Further longitudinal studies are needed to determine the utility of the 3-way clustering method with multimodal biological biomarkers.

Machine learning-based classification of Alzheimer's disease and its at-risk states using personality traits, anxiety, and depression

BACKGROUND

Alzheimer's disease (AD) is often preceded by stages of cognitive impairment, namely subjective cognitive decline (SCD) and mild cognitive impairment (MCI). While cerebrospinal fluid (CSF) biomarkers are established predictors of AD, other non-invasive candidate predictors include personality traits, anxiety, and depression, among others. These predictors offer non-invasive assessment and exhibit changes during AD development and preclinical stages.

METHODS

In a cross-sectional design, we comparatively evaluated the predictive value of personality traits (Big Five), geriatric anxiety and depression scores, resting-state functional magnetic resonance imaging activity of the default mode network, apoliprotein E (ApoE) genotype, and CSF biomarkers (tTau, pTau181, Aβ42/40 ratio) in a multi-class support vector machine classification. Participants included 189 healthy controls (HC), 338 individuals with SCD, 132 with amnestic MCI, and 74 with mild AD from the multicenter DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE).

RESULTS

Mean predictive accuracy across all participant groups was highest when utilizing a combination of personality, depression, and anxiety scores. HC were best predicted by a feature set comprised of depression and anxiety scores and participants with AD were best predicted by a feature set containing CSF biomarkers. Classification of participants with SCD or aMCI was near chance level for all assessed feature sets.

CONCLUSION

Our results demonstrate predictive value of personality trait and state scores for AD. Importantly, CSF biomarkers, personality, depression, anxiety, and ApoE genotype show complementary value for classification of AD and its at-risk stages.

Deep learning for risk-based stratification of cognitively impaired individuals

Quantifying the risk of progression to Alzheimer's disease (AD) could help identify persons who could benefit from early interventions. We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 544, discovery cohort) and the National Alzheimer's Coordinating Center (NACC, n = 508, validation cohort), subdividing individuals with mild cognitive impairment (MCI) into risk groups based on cerebrospinal fluid amyloid-β levels and identifying differential gray matter patterns. We then created models that fused neural networks with survival analysis, trained using non-parcellated T1-weighted brain MRIs from ADNI data, to predict the trajectories of MCI to AD conversion within the NACC cohort (integrated Brier score: 0.192 [discovery], and 0.108 [validation]). Using modern interpretability techniques, we verified that regions important for model prediction are classically associated with AD. We confirmed AD diagnosis labels using postmortem data. We conclude that our framework provides a strategy for risk-based stratification of individuals with MCI and for identifying regions key for disease prognosis.

Longitudinal changes in metabolic network activity in early Alzheimer's disease

INTRODUCTION

The progression of Alzheimer's disease (AD) has been linked to two metabolic networks, the AD-related pattern (ADRP) and the default mode network (DMN).

METHODS

Converting and clinically stable cognitively normal subjects (n = 47) and individuals with mild cognitive impairment (n = 96) underwent 2-[18 F]fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET) three or more times over 6 years (nscans  = 705). Expression levels for ADRP and DMN were measured in each subject and time point, and the resulting changes were correlated with cognitive performance. The role of network expression in predicting conversion to dementia was also evaluated.

RESULTS

Longitudinal increases in ADRP expression were observed in converters, while age-related DMN loss was seen in converters and nonconverters. Cognitive decline correlated with increases in ADRP and declines in DMN, but conversion to dementia was predicted only by baseline ADRP levels.

DISCUSSION

The results point to the potential utility of ADRP as an imaging biomarker of AD progression.

Associations of Blood and Cerebrospinal Fluid Aβ and tau Levels with Renal Function

Amyloid β (Aβ) and tau play pivotal roles in the pathogenesis of Alzheimer's disease (AD). Previous studies have shown that brain-derived Aβ and tau can be cleared through transport into the periphery, and the kidneys may be vital organs involved in the clearance of Aβ and tau. However, the effects of deficiency in the clearance of Aβ and tau by the kidneys on brain AD-type pathologies in humans remain largely unknown. In this study, we first recruited 41 patients with chronic kidney disease (CKD) and 40 age- and sex-matched controls with normal renal function to analyze the associations of the estimated glomerular filtration rate (eGFR) with plasma Aβ and tau levels. To analyze the associations of eGFR with cerebrospinal fluid (CSF) AD biomarkers, we recruited 42 cognitively normal CKD patients and 150 cognitively normal controls with CSF samples. Compared with controls with normal renal function, CKD patients had higher plasma levels of Aβ40, Aβ42 and total tau (T-tau), lower CSF levels of Aβ40 and Aβ42 and higher levels of CSF T-tau/Aβ42 and phosphorylated tau (P-tau)/Aβ42. Plasma Aβ40, Aβ42, and T-tau levels were negatively correlated with eGFR. In addition, eGFR was negatively correlated with CSF levels of T-tau, T-tau/Aβ42, and P-tau/Aβ42 but positively correlated with Mini-Mental State Examination (MMSE) scores. Thus, this study showed that the decline in renal function was correlated with abnormal AD biomarkers and cognitive decline, which provides human evidence that renal function may be involved in the pathogenesis of AD.

Genome-Wide Meta-Analysis of Cerebrospinal Fluid Biomarkers in Alzheimer's Disease and Parkinson's Disease Cohorts

BACKGROUND

Amyloid-β, phosphorylated tau (p-tau), and total tau (t-tau) in cerebrospinal fluid are established biomarkers for Alzheimer's disease (AD). In other neurodegenerative diseases, such as Parkinson's disease (PD), these biomarkers have also been found to be altered, and the molecular mechanisms responsible for these alterations are still under investigation. Moreover, the interplay between these mechanisms and the diverse underlying disease states remains to be elucidated.

OBJECTIVE

To investigate genetic contributions to the AD biomarkers and assess the commonality and heterogeneity of the associations per underlying disease status.

METHODS

We conducted genome-wide association studies (GWASs) for the AD biomarkers on subjects from the Parkinson's Progression Markers Initiative, the Fox Investigation for New Discovery of Biomarkers, and the Alzheimer's Disease Neuroimaging Initiative, and meta-analyzed with the largest AD GWAS. We tested heterogeneity of associations of interest between different disease statuses (AD, PD, and control).

RESULTS

We observed three GWAS signals: the APOE locus for amyloid-β, the 3q28 locus between GEMC1 and OSTN for p-tau and t-tau, and the 7p22 locus (top hit: rs60871478, an intronic variant for DNAAF5, also known as HEATR2) for p-tau. The 7p22 locus is novel and colocalized with the brain DNAAF5 expression. Although no heterogeneity from underlying disease status was observed for the earlier GWAS signals, some disease risk loci suggested disease-specific associations with these biomarkers.

CONCLUSIONS

Our study identified a novel association at the intronic region of DNAAF5 associated with increased levels of p-tau across all diseases. We also observed some disease-specific genetic associations with these biomarkers. Published 2023. This article is a U.S. Government work and is in the public domain in the USA.

Improved connectivity and cognition due to cognitive stimulation in Alzheimer's disease

Background

Due to the increasing prevalence of Alzheimer's disease (AD) and the limited efficacy of pharmacological treatment, the interest in non-pharmacological interventions, e.g., cognitive stimulation therapy (CST), to improve cognitive dysfunction and the quality of life of AD patients are on a steady rise.

Objectives

Here, we examined the efficacy of a CST program specifically conceptualized for AD dementia patients and the neural mechanisms underlying cognitive or behavioral benefits of CST.

Methods

Using neuropsychological tests and MRI-based measurements of functional connectivity, we examined the (neuro-) psychological status and network changes at two time points: pre vs. post-stimulation (8 to 12 weeks) in the intervention group (n = 15) who received the CST versus a no-intervention control group (n = 15).

Results

After CST, we observed significant improvement in the Mini-Mental State Examination (MMSE), the Alzheimer's Disease Assessment Scale, cognitive subsection (ADAS-cog), and the behavioral and psychological symptoms of dementia (BPSD) scores. These cognitive improvements were associated with an up-regulated functional connectivity between the left posterior hippocampus and the trunk of the left postcentral gyrus.

Conclusion

Our data indicate that CST seems to induce short-term global cognition and behavior improvements in mild to moderate AD dementia and enhances resting-state functional connectivity in learning- and memory-associated brain regions. These convergent results prove that even in mild to moderate dementia AD, neuroplasticity can be harnessed to alleviate cognitive impairment with CST.

Risk of Alzheimer's Disease is Associated with Longitudinal Changes in Plasma Biomarkers in the Multiethnic Washington Heights, Inwood Columbia Aging Project Cohort

INTRODUCTION

Alzheimer's disease (AD) biomarkers can help differentiate cognitively unimpaired (CU) individuals from mild cognitive impairment (MCI) and dementia. The role of AD biomarkers in predicting cognitive impairment and AD needs examination.

METHODS

In 628 CU individuals from a multi-ethnic cohort, Aβ42, Aβ40, phosphorylated tau-181 (P-tau181), glial fibrillary acid protein (GFAP), and neurofilament light chain (NfL) were measured in plasma.

RESULTS

Higher baseline levels of P-tau181/Aβ42 ratio were associated with increased risk of incident dementia. A biomarker pattern (with elevated Aβ42/Aβ40 but low P-tau181/Aβ42) was associated with decreased dementia risk. Compared to CU, participants who developed MCI or dementia had a rapid decrease in the biomarker pattern reflecting AD-specific pathological change.

DISCUSSION

Elevated levels of AD biomarker P-tau181/Aβ42, by itself or combined with a low Aβ42/Aβ40 level, predicts clinically diagnosed AD. Individuals with a rapid change in these biomarkers may need close monitoring for the potential downward trajectory of cognition.

Associations between Neuropsychiatric Symptoms and Alzheimer's Disease Biomarkers in People with Mild Cognitive Impairment

BACKGROUND

Neuropsychiatric symptoms (NPS) are associated with faster decline in mild cognitive impairment (MCI). This study aimed to investigate the association between NPS severity and Alzheimer's disease (AD) biomarkers, i.e., amyloid-β (Aβ), phosphorylated tau protein (p-tau) and hippocampal volume ratio (HR), to characterise in more detail MCI patients with a poor prognosis.

METHODS

A total of 506 individuals with MCI and 99 cognitively unimpaired older adults were selected from the ADNI dataset. The patients were divided into three different groups based on their NPI-Q total scores: no NPS (n = 198), mild NPS (n = 160) and severe NPS (n = 148). Regression models were used to assess the association between the severity of NPS and each biomarker level and positivity status.

RESULTS

Cerebrospinal fluid Aβ levels were positively associated with older age and lower MMSE scores, while higher p-tau levels were associated with female sex and lower MMSE scores. Only patients with severe NPS had a lower HR (β = -0.18, p = 0.050), i.e., more pronounced medio-temporal atrophy, than those without NPS.

DISCUSSION

Only HR was associated with the presence of NPS, partially in line with previous evidence showing that severe NPS may be explained primarily by greater grey matter loss. Future longitudinal studies will be needed to ascertain the relevance of this finding.

Clinical characteristics of patients with suspected Alzheimer's disease within a CSF Aß-ratio grey zone

BACKGROUND

The AT(N) research framework for Alzheimer's disease (AD) remains unclear on how to best deal with borderline cases. Our aim was to characterise patients with suspected AD with a borderline Aß1-42/Aß1-40 ratio in cerebrospinal fluid.

METHODS

We analysed retrospective data from two cohorts (memory clinic cohort and ADNI) of patients (n = 63) with an Aß1-42/Aß1-40 ratio within a predefined borderline area-Q1 above the validated cut-off value(grey zone). We compared demographic, clinical, neuropsychological and neuroimaging features between grey zone patients and patients with low Aß1-42 (normal Aß ratio but pathological Aß1-42, n = 42) and patients with AD (pathological Aß, P-Tau, und T-Tau, n = 80).

RESULTS

Patients had mild cognitive impairment or mild dementia and a median age of 72 years. Demographic and general clinical characteristics did not differ between the groups. Patients in the grey zone group were the least impaired in cognition. However, they overlapped with the low Aß1-42 group in verbal episodic memory performance, especially in delayed recall and recognition. The grey zone group had less severe medial temporal atrophy, but mild posterior atrophy and mild white matter hyperintensities, similar to the low Aß1-42 group.

CONCLUSIONS

Patients in the Aß ratio grey zone were less impaired, but showed clinical overlap with patients on the AD continuum. These borderline patients may be at an earlier disease stage. Assuming an increased risk of AD and progressive cognitive decline, careful consideration of clinical follow-up is recommended when using dichotomous approaches to classify Aß status.

Age-related and amyloid-beta-independent tau deposition and its downstream effects

Amyloid-β (Aβ) is hypothesized to facilitate the spread of tau pathology beyond the medial temporal lobe. However, there is evidence that, independently of Aβ, age-related tau pathology might be present outside of the medial temporal lobe. We therefore aimed to study age-related Aβ-independent tau deposition outside the medial temporal lobe in two large cohorts and to investigate potential downstream effects of this on cognition and structural measures. We included 545 cognitively unimpaired adults (40-92 years) from the BioFINDER-2 study (in vivo) and 639 (64-108 years) from the Rush Alzheimer's Disease Center cohorts (ex vivo). 18F-RO948- and 18F-flutemetamol-PET standardized uptake value ratios were calculated for regional tau and global/regional Aβ in vivo. Immunohistochemistry was used to estimate Aβ load and tangle density ex vivo. In vivo medial temporal lobe volumes (subiculum, cornu ammonis 1) and cortical thickness (entorhinal cortex, Brodmann area 35) were obtained using Automated Segmentation for Hippocampal Subfields packages. Thickness of early and late neocortical Alzheimer's disease regions was determined using FreeSurfer. Global cognition and episodic memory were estimated to quantify cognitive functioning. In vivo age-related tau deposition was observed in the medial temporal lobe and in frontal and parietal cortical regions, which was statistically significant when adjusting for Aβ. This was also observed in individuals with low Aβ load. Tau deposition was negatively associated with cortical volumes and thickness in temporal and parietal regions independently of Aβ. The associations between age and cortical volume or thickness were partially mediated via tau in regions with early Alzheimer's disease pathology, i.e. early tau and/or Aβ pathology (subiculum/Brodmann area 35/precuneus/posterior cingulate). Finally, the associations between age and cognition were partially mediated via tau in Brodmann area 35, even when including Aβ-PET as covariate. Results were validated in the ex vivo cohort showing age-related and Aβ-independent increases in tau aggregates in and outside the medial temporal lobe. Ex vivo age-cognition associations were mediated by medial and inferior temporal tau tangle density, while correcting for Aβ density. Taken together, our study provides support for primary age-related tauopathy even outside the medial temporal lobe in vivo and ex vivo, with downstream effects on structure and cognition. These results have implications for our understanding of the spreading of tau outside the medial temporal lobe, also in the context of Alzheimer's disease. Moreover, this study suggests the potential utility of tau-targeting treatments in primary age-related tauopathy, likely already in preclinical stages in individuals with low Aβ pathology.

CSF biomarker concordance with amyloid PET in Phase 3 studies of aducanumab

INTRODUCTION

We assessed the use of cerebrospinal fluid (CSF) biomarkers as an alternative to positron emission tomography (PET) for brain amyloid beta (Aβ) pathology confirmation in the EMERGE and ENGAGE clinical trials.

METHODS

EMERGE and ENGAGE were randomized, placebo-controlled, Phase 3 trials of aducanumab in participants with early Alzheimer's disease. Concordance between CSF biomarkers (Aβ42, Aβ40, phosphorylated tau 181, and total tau) and amyloid PET status (visual read) at screening was examined.

RESULTS

Robust concordance between CSF biomarkers and amyloid PET visual status was observed (for Aβ42/Aβ40, AUC: 0.90; 95% CI: 0.83-0.97; p < 0.0001), confirming CSF biomarkers as a reliable alternative to amyloid PET in these studies. Compared with single CSF biomarkers, CSF biomarker ratios showed better agreement with amyloid PET visual reads, demonstrating high diagnostic accuracy.

DISCUSSION

These analyses add to the growing body of evidence supporting CSF biomarkers as reliable alternatives to amyloid PET imaging for brain Aβ pathology confirmation.

HIGHLIGHTS

CSF biomarkers and amyloid PET concordance were assessed in Ph3 aducanumab trials. Robust concordance between CSF biomarkers and amyloid PET was observed. CSF biomarker ratios increased diagnostic accuracy over single CSF biomarkers. CSF Aβ42/Aβ40 demonstrated high concordance with amyloid PET. Results support CSF biomarker testing as a reliable alternative to amyloid PET.

Differences between ante mortem Alzheimer's disease biomarkers in predicting neuropathology at autopsy

INTRODUCTION

This study aimed to assess whether biomarkers related to amyloid, tau, and neurodegeneration can accurately predict Alzheimer's disease (AD) neuropathology at autopsy in early and late clinical stages.

METHODS

We included 100 participants who had ante mortem biomarker measurements and underwent post mortem neuropathological examination. Based on ante mortem clinical diagnosis, participants were divided into non-dementia and dementia, as early or late clinical stages.

RESULTS

Amyloid positron emission tomography (PET) and cerebrospinal fluid (CSF) amyloid beta (Aβ)42/phosphorylated tau (p-tau)181 showed excellent performance in differentiating autopsy-confirmed AD and predicting the risk of neuropathological changes in early and late clinical stages. However, CSF Aβ42 performed better in the early clinical stage, while CSF p-tau181, CSF t-tau, and plasma p-tau181 performed better in the late clinical stage.

DISCUSSION

Our findings provide important clinical information that, if using PET, CSF, and plasma biomarkers to detect AD pathology, researchers must consider their differential performances at different clinical stages of AD.

HIGHLIGHTS

Amyloid PET and CSF Aβ42/p-tau181 were the most promising candidate biomarkers for predicting AD pathology. CSF Aβ42 can serve as a candidate predictive biomarker in the early clinical stage of AD. CSF p-tau181, CSF t-tau, and plasma p-tau181 can serve as candidate predictive biomarkers in the late clinical stage of AD. Combining APOE ε4 genotypes can significantly improve the predictive accuracy of AD-related biomarkers for AD pathology.

Case-control virtual histology elucidates cell types associated with cortical thickness differences in Alzheimer's disease

Many neuropsychiatric disorders are characterised by altered cortical thickness, but the cell types underlying these changes remain largely unknown. Virtual histology (VH) approaches map regional patterns of gene expression with regional patterns of MRI-derived phenotypes, such as cortical thickness, to identify cell types associated with case-control differences in those MRI measures. However, this method does not incorporate valuable information of case-control differences in cell type abundance. We developed a novel method, termed case-control virtual histology (CCVH), and applied it to Alzheimer's disease (AD) and dementia cohorts. Leveraging a multi-region gene expression dataset of AD cases (n = 40) and controls (n = 20), we quantified AD case-control differential expression of cell type-specific markers across 13 brain regions. We then correlated these expression effects with MRI-derived AD case-control cortical thickness differences across the same regions. Cell types with spatially concordant AD-related effects were identified through resampling marker correlation coefficients. Among regions thinner in AD, gene expression patterns identified by CCVH suggested fewer excitatory and inhibitory neurons, and greater proportions of astrocytes, microglia, oligodendrocytes, oligodendrocyte precursor cells, and endothelial cells in AD cases vs. controls. In contrast, original VH identified expression patterns suggesting that excitatory but not inhibitory neuron abundance was associated with thinner cortex in AD, despite the fact that both types of neurons are known to be lost in the disorder. Compared to original VH, cell types identified through CCVH are more likely to directly underlie cortical thickness differences in AD. Sensitivity analyses suggest our results are largely robust to specific analysis choices, like numbers of cell type-specific marker genes used and background gene sets used to construct null models. As more multi-region brain expression datasets become available, CCVH will be useful for identifying the cellular correlates of cortical thickness across neuropsychiatric illnesses.

Neuropathology-based APOE genetic risk score better quantifies Alzheimer's risk

INTRODUCTION

Apolipoprotein E (APOE) ε4-carrier status or ε4 allele count are included in analyses to account for the APOE genetic effect on Alzheimer's disease (AD); however, this does not account for protective effects of APOE ε2 or heterogeneous effect of ε2, ε3, and ε4 haplotypes.

METHODS

We leveraged results from an autopsy-confirmed AD study to generate a weighted risk score for APOE (APOE-npscore). We regressed cerebrospinal fluid (CSF) amyloid and tau biomarkers on APOE variables from the Wisconsin Registry for Alzheimer's Prevention (WRAP), Wisconsin Alzheimer's Disease Research Center (WADRC), and Alzheimer's Disease Neuroimaging Initiative (ADNI).

RESULTS

The APOE-npscore explained more variance and provided a better model fit for all three CSF measures than APOE ε4-carrier status and ε4 allele count. These findings were replicated in ADNI and observed in subsets of cognitively unimpaired (CU) participants.

DISCUSSION

The APOE-npscore reflects the genetic effect on neuropathology and provides an improved method to account for APOE in AD-related analyses.