Joint effects of air pollution and neighborhood socioeconomic status on cognitive decline - Mediation by depression, high cholesterol levels, and high blood pressure

Air pollution and neighborhood socioeconomic status (N-SES) are associated with adverse cardiovascular health and neuropsychiatric functioning in older adults. This study examines the degree to which the joint effects of air pollution and N-SES on the cognitive decline are mediated by high cholesterol levels, high blood pressure (HBP), and depression. In the Emory Healthy Aging Study, 14,390 participants aged 50+ years from Metro Atlanta, GA, were assessed for subjective cognitive decline using the cognitive function instrument (CFI). Information on the prior diagnosis of high cholesterol, HBP, and depression was collected through the Health History Questionnaire. Participants' census tracts were assigned 3-year average concentrations of 12 air pollutants and 16 N-SES characteristics. We used the unsupervised clustering algorithm Self-Organizing Maps (SOM) to create 6 exposure clusters based on the joint distribution of air pollution and N-SES in each census tract. Linear regression analysis was used to estimate the effects of the SOM cluster indicator on CFI, adjusting for age, race/ethnicity, education, and neighborhood residential stability. The proportion of the association mediated by high cholesterol levels, HBP, and depression was calculated by comparing the total and direct effects of SOM clusters on CFI. Depression mediated up to 87 % of the association between SOM clusters and CFI. For example, participants living in the high N-SES and high air pollution cluster had CFI scores 0.05 (95 %-CI:0.01,0.09) points higher on average compared to those from the high N-SES and low air pollution cluster; after adjusting for depression, this association was attenuated to 0.01 (95 %-CI:-0.04,0.05). HBP mediated up to 8 % of the association between SOM clusters and CFI and high cholesterol up to 5 %. Air pollution and N-SES associated cognitive decline was partially mediated by depression. Only a small portion (<10 %) of the association was mediated by HBP and high cholesterol.

Bridging the Gap: Multi-Omics Profiling of Brain Tissue in Alzheimer's Disease and Older Controls in Multi-Ethnic Populations

INTRODUCTION

Multi-omics studies in Alzheimer's disease (AD) revealed many potential disease pathways and therapeutic targets. Despite their promise of precision medicine, these studies lacked African Americans (AA) and Latin Americans (LA), who are disproportionately affected by AD.

METHODS

To bridge this gap, Accelerating Medicines Partnership in AD (AMP-AD) expanded brain multi-omics profiling to multi-ethnic donors.

RESULTS

We generated multi-omics data and curated and harmonized phenotypic data from AA (n=306), LA (n=326), or AA and LA (n=4) brain donors plus Non-Hispanic White (n=252) and other (n=20) ethnic groups, to establish a foundational dataset enriched for AA and LA participants. This study describes the data available to the research community, including transcriptome from three brain regions, whole genome sequence, and proteome measures.

DISCUSSION

Inclusion of traditionally underrepresented groups in multi-omics studies is essential to discover the full spectrum of precision medicine targets that will be pertinent to all populations affected with AD.

APOE loss-of-function variants: Compatible with longevity and associated with resistance to Alzheimer's disease pathology

The ε4 allele of apolipoprotein E (APOE) is the strongest genetic risk factor for sporadic Alzheimer's disease (AD). Knockdown of ε4 may provide a therapeutic strategy for AD, but the effect of APOE loss of function (LoF) on AD pathogenesis is unknown. We searched for APOE LoF variants in a large cohort of controls and patients with AD and identified seven heterozygote carriers of APOE LoF variants. Five carriers were controls (aged 71-90 years), one carrier was affected by progressive supranuclear palsy, and one carrier was affected by AD with an unremarkable age at onset of 75 years. Two APOE ε3/ε4 controls carried a stop-gain affecting ε4: one was cognitively normal at 90 years and had no neuritic plaques at autopsy; the other was cognitively healthy at 79 years, and lumbar puncture at 76 years showed normal levels of amyloid. These results suggest that ε4 drives AD risk through the gain of abnormal function and support ε4 knockdown as a viable therapeutic option.

Examining amyloid reduction as a surrogate endpoint through latent class analysis using clinical trial data for dominantly inherited Alzheimer's disease

INTRODUCTION

Increasing evidence suggests that amyloid reduction could serve as a plausible surrogate endpoint for clinical and cognitive efficacy. The double-blind phase 3 DIAN-TU-001 trial tested clinical and cognitive declines with increasing doses of solanezumab or gantenerumab.

METHODS

We used latent class (LC) analysis on data from the Dominantly Inherited Alzheimer Network Trials Unit 001 trial to test amyloid positron emission tomography (PET) reduction as a potential surrogate biomarker.

RESULTS

LC analysis categorized participants into three classes: amyloid no change, amyloid reduction, and amyloid growth, based on longitudinal amyloid Pittsburgh compound B PET standardized uptake value ratio data. The amyloid-no-change class was at an earlier disease stage for amyloid amounts and dementia. Despite similar baseline characteristics, the amyloid-reduction class exhibited reductions in the annual decline rates compared to the amyloid-growth class across multiple biomarker, clinical, and cognitive outcomes.

DISCUSSION

LC analysis indicates that amyloid reduction is associated with improved clinical outcomes and supports its use as a surrogate biomarker in clinical trials.

HIGHLIGHTS

We used latent class (LC) analysis to test amyloid reduction as a surrogate biomarker. Despite similar baseline characteristics, the amyloid-reduction class exhibited remarkably better outcomes compared to the amyloid-growth class across multiple measures. LC analysis proves valuable in testing amyloid reduction as a surrogate biomarker in clinical trials lacking significant treatment effects.

Alzheimer's disease cerebrospinal fluid biomarkers and kidney function in normal and cognitively impaired older adults

INTRODUCTION

Recent Alzheimer's disease (AD) clinical trials have used cerebrospinal fluid (CSF) biomarker levels for screening and enrollment. Preliminary evidence suggests that AD risk is related to impaired renal function. The impact of kidney function on commonly used AD biomarkers remains unknown.

METHODS

Participants in studies conducted at the Goizueta Alzheimer's Disease Research Center (N = 973) had measurements of serum creatinine and CSF AD biomarkers. General linear models and individual data were used to assess the relationships between biomarkers and eGFR.

RESULTS

Lower estimated glomerular filtration rate (eGFR) was associated with lower amyloid beta (Aβ)42/tau ratio (p < 0.0001) and Aβ42 (p = 0.002) and higher tau (p < 0.0001) and p-tau (p = 0.0002). The impact of eGFR on AD biomarker levels was more robust in individuals with cognitive impairment (all p-values were < 0.005).

DISCUSSION

The association between eGFR and CSF AD biomarkers has a significant impact that varies by cognitive status. Future studies exploring this impact on the pathogenesis of AD and related biomarkers are needed.

Highlights

There is a significant association between Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarkers and both estimated glomerular filtration rate (eGFR) and mild cognitive impairment (MCI).Kidney function influences CSF biomarker levels in individuals with normal cognitive function and those with MCI.The impact of kidney function on AD biomarker levels is more pronounced in individuals with cognitive impairment.The variation in CSF tau levels is independent of cardiovascular factors and is likely directly related to kidney function.Tau may have a possible role in both kidney and cognitive function.

Association between Fine Particulate Matter Exposure and Cerebrospinal Fluid Biomarkers of Alzheimer's Disease among a Cognitively Healthy Population-Based Cohort

BACKGROUND

Epidemiological evidence suggests air pollution adversely affects cognition and increases the risk of Alzheimer's disease (AD), but little is known about the biological effects of fine particulate matter (PM 2.5 , particulate matter with aerodynamic diameter ≤ 2.5 μ m ) on early predictors of future disease risk.

OBJECTIVES

We investigated the association between 1-, 3-, and 5-y exposure to ambient and traffic-related PM 2.5 and cerebrospinal fluid (CSF) biomarkers of AD.

METHODS

We conducted a cross-sectional analysis using data from 1,113 cognitively healthy adults (45-75 y of age) from the Emory Healthy Brain Study in Georgia in the United States. CSF biomarker concentrations of A β 42 , tTau, and pTau, were collected at enrollment (2016-2020) and analyzed with the Roche Elecsys system. Annual ambient and traffic-related residential PM 2.5 concentrations were estimated at a 1 -km and 250 -m resolution, respectively, and computed for each participant's geocoded address, using three exposure time periods based on specimen collection date. Associations between PM 2.5 and CSF biomarker concentrations, considering continuous and dichotomous (dichotomized at clinical cutoffs) outcomes, were estimated with multiple linear/logistic regression, respectively, controlling for potential confounders (age, gender, race, ethnicity, body mass index, and neighborhood socioeconomic status).

RESULTS

Interquartile range (IQR; IQR = 0.845 ) increases in 1-y [β : - 0.101 ; 95% confidence interval (CI): - 0.18 , - 0.02 ] and 3-y (β : - 0.078 ; 95% CI: - 0.15 , - 0.00 ) ambient PM 2.5 exposures were negatively associated with A β 42 CSF concentrations. Associations between ambient PM 2.5 and A β 42 were similar for 5-y estimates (β : - 0.076 ; 95% CI: - 0.160 , 0.005). Dichotomized CSF variables revealed similar associations between ambient PM 2.5 and A β 42 . Associations with traffic-related PM 2.5 were similar but not significant. Associations between PM 2.5 exposures and tTau, pTau tTau / A β 42 , or pTau / A β 42 levels were mainly null.

CONCLUSION

In our study, consistent trends were found between 1-y PM 2.5 exposure and decreased CSF A β 42 , which suggests an accumulation of amyloid plaques in the brain and an increased risk of developing AD. https://doi.org/10.1289/EHP13503.

Disentangling Visual Exploration Differences in Cognitive Impairment

OBJECTIVE

Individuals with cognitive impairment (CI) exhibit different oculomotor functions and viewing behaviors. In this work we aimed to quantify the differences in these functions with CI severity, and assess general CI and specific cognitive functions related to visual exploration behaviors.

METHODS

A validated passive viewing memory test with eyetracking was administered to 348 healthy controls and CI individuals. Spatiotemporal properties of the scanpath, the semantic category of the viewed regions, and other composite features were extracted from the estimated eyegaze locations on the corresponding pictures displayed during the test. These features were then used to characterize viewing patterns, classify cognitive impairment, and estimate scores in various neuropsychological tests using machine learning.

RESULTS

Statistically significant differences in spatial, spatiotemporal, and semantic features were found between healthy controls and individuals with CI. The CI group spent more time gazing at the center of the image, looked at more regions of interest (ROI), transitioned less often between ROI yet in a more unpredictable manner, and exhibited different semantic preferences. A combination of these features achieved an area under the receiver-operator curve of 0.78 in differentiating CI individuals from controls. Statistically significant correlations were identified between actual and estimated CI scores and other neuropsychological tests.

CONCLUSION

Evaluating visual exploration behaviors provided quantitative and systematic evidence of differences in CI individuals, leading to an improved approach for passive cognitive impairment screening.

SIGNIFICANCE

The proposed passive, accessible, and scalable approach could help with earlier detection and a better understanding of cognitive impairment.

Differential DNA methylation in the brain as potential mediator of the association between traffic-related PM2.5 and neuropathology markers of Alzheimer's disease

INTRODUCTION

Growing evidence indicates that fine particulate matter (PM2.5) is a risk factor for Alzheimer's disease (AD), but the underlying mechanisms have been insufficiently investigated. We hypothesized differential DNA methylation (DNAm) in brain tissue as a potential mediator of this association.

METHODS

We assessed genome-wide DNAm (Illumina EPIC BeadChips) in prefrontal cortex tissue and three AD-related neuropathological markers (Braak stage, CERAD, ABC score) for 159 donors, and estimated donors' residential traffic-related PM2.5 exposure 1, 3, and 5 years prior to death. We used a combination of the Meet-in-the-Middle approach, high-dimensional mediation analysis, and causal mediation analysis to identify potential mediating CpGs.

RESULTS

PM2.5 was significantly associated with differential DNAm at cg25433380 and cg10495669. Twenty-four CpG sites were identified as mediators of the association between PM2.5 exposure and neuropathology markers, several located in genes related to neuroinflammation.

DISCUSSION

Our findings suggest differential DNAm related to neuroinflammation mediates the association between traffic-related PM2.5 and AD.

HIGHLIGHTS

First study to evaluate the potential mediation effect of DNA methylation for the association between PM2.5 exposure and neuropathological changes of Alzheimer's disease. Study was based on brain tissues rarely investigated in previous air pollution research. Cg10495669, assigned to RBCK1 gene playing a role in inflammation, was associated consistently with 1-year, 3-year, and 5-year traffic-related PM2.5 exposures prior to death. Meet-in-the-middle approach and high-dimensional mediation analysis were used simultaneously to increase the potential of identifying the differentially methylated CpGs. Differential DNAm related to neuroinflammation was found to mediate the association between traffic-related PM2.5 and Alzheimer's disease.

Association of PM2.5 Exposure and Alzheimer Disease Pathology in Brain Bank Donors-Effect Modification by APOE Genotype

BACKGROUND AND OBJECTIVES

Fine particulate matter (PM2.5) exposure has been found to be associated with Alzheimer disease (AD) and is hypothesized to cause inflammation and oxidative stress in the brain, contributing to neuropathology. The APOE gene, a major genetic risk factor of AD, has been hypothesized to modify the association between PM2.5 and AD. However, little prior research exists to support these hypotheses. This study investigates the association between traffic-related PM2.5 and AD hallmark pathology, including effect modification by APOE genotype, in an autopsy cohort.

METHODS

A cross-sectional study was conducted using brain tissue donors enrolled in the Emory Goizueta AD Research Center who died before 2020 (n = 224). Donors were assessed for AD pathology including the Braak stage, Consortium to Establish a Registry for AD (CERAD) score, and combined AD neuropathologic change (ABC) score. Traffic-related PM2.5 concentrations were modeled for the metro-Atlanta area during 2002-2019 with a spatial resolution of 200-250 m. One-year, 3-year, and 5-year average PM2.5 concentrations before death were matched to participants' home address. We assessed the association between traffic-related PM2.5 and AD hallmark pathology and effect modification by APOE genotype, using adjusted ordinal logistic regression models.

RESULTS

Among the 224 participants, the mean age of death was 76 years, and 57% had at least 1 APOE ε4 copy. Traffic-related PM2.5 was significantly associated with the CERAD score for the 1-year exposure window (odds ratio [OR] 1.92; 95% CI 1.12-3.30) and the 3-year exposure window (OR 1.87; 95% CI 1.01-3.17). PM2.5 was also associated with higher Braak stage and ABC score albeit nonsignificantly. The strongest associations between PM2.5 and neuropathology markers were among those without APOE ε4 alleles (e.g., for the CERAD score and 1-year exposure window, OR 2.31; 95% CI 1.36-3.94), though interaction between PM2.5 and APOE genotype was not statistically significant.

DISCUSSION

Our study found traffic-related PM2.5 exposure was associated with the CERAD score in an autopsy cohort, contributing to epidemiologic evidence that PM2.5 affects β-amyloid deposition in the brain. This association was particularly strong among donors without APOE ε4 alleles. Future studies should further investigate the biological mechanisms behind this association.

Relationships of Cognitive Measures with Cerebrospinal Fluid but Not Imaging Biomarkers of Alzheimer Disease Vary between Black and White Individuals

OBJECTIVE

Biomarkers of Alzheimer disease vary between groups of self-identified Black and White individuals in some studies. This study examined whether the relationships between biomarkers or between biomarkers and cognitive measures varied by racialized groups.

METHODS

Cerebrospinal fluid (CSF), amyloid positron emission tomography (PET), and magnetic resonance imaging measures were harmonized across four studies of memory and aging. Spearman correlations between biomarkers and between biomarkers and cognitive measures were calculated within each racialized group, then compared between groups by standard normal tests after Fisher's Z-transformations.

RESULTS

The harmonized dataset included at least one biomarker measurement from 495 Black and 2,600 White participants. The mean age was similar between racialized groups. However, Black participants were less likely to have cognitive impairment (28% vs 36%) and had less abnormality of some CSF biomarkers including CSF Aβ42/40, total tau, p-tau181, and neurofilament light. CSF Aβ42/40 was negatively correlated with total tau and p-tau181 in both groups, but at a smaller magnitude in Black individuals. CSF Aβ42/40, total tau, and p-tau181 had weaker correlations with cognitive measures, especially episodic memory, in Black than White participants. Correlations of amyloid measures between CSF (Aβ42/40, Aβ42) and PET imaging were also weaker in Black than White participants. Importantly, no differences based on race were found in correlations between different imaging biomarkers, or in correlations between imaging biomarkers and cognitive measures.

INTERPRETATION

Relationships between CSF biomarkers but not imaging biomarkers varied by racialized groups. Imaging biomarkers performed more consistently across racialized groups in associations with cognitive measures. ANN NEUROL 2024;95:495-506.

Heparin-enriched plasma proteome is significantly altered in Alzheimer's Disease

Introduction

Heparin binding proteins (HBPs) with roles in extracellular matrix assembly are strongly correlated to β-amyloid (Aβ) and tau pathology in Alzheimer's disease (AD) brain and cerebrospinal fluid (CSF). However, it remains challenging to detect these proteins in plasma using standard mass spectrometry-based proteomic approaches.

Methods

We employed heparin affinity chromatography, followed by off-line fractionation and tandem mass tag mass spectrometry (TMT-MS), to capture and enrich HBPs in plasma obtained from AD (n=62) and control (n=47) samples. These profiles were then correlated to a consensus AD brain proteome, as well as with Aβ, tau and phosphorylated tau (pTau) CSF biomarkers from the same individuals. We then leveraged published human postmortem brain proteome datasets to assess the overlap with the heparin-enriched plasma proteome.

Results

Heparin-enrichment from plasma was highly reproducible, enriched well-known HBPs like APOE and thrombin, and depleted high-abundance proteins such as albumin. A total of 2865 proteins, spanning 10 orders of magnitude were detectable. Utilizing a consensus AD brain protein co-expression network, we observed that specific plasma HBPs exhibited consistent direction of change in both brain and plasma, whereas others displayed divergent changes highlighting the complex interplay between the two compartments. Elevated HBPs in AD plasma, when compared to controls, included members of the matrisome module in brain that accumulate within Aβ deposits, such as SMOC1, SMOC2, SPON1, MDK, OLFML3, FRZB, GPNMB, and APOE. Additionally, heparin enriched plasma proteins demonstrated significant correlations with conventional AD CSF biomarkers, including Aβ, total tau, pTau, and plasma pTau from the same individuals.

Conclusion

These findings support the utility of a heparin-affinity approach for enriching amyloid-associated proteins, as well as a wide spectrum of plasma biomarkers that reflect pathological changes in the AD brain.

Network Proteomics of the Lewy Body Dementia Brain Reveals Presynaptic Signatures Distinct from Alzheimer's Disease

Lewy body dementia (LBD), a class of disorders comprising Parkinson's disease dementia (PDD) and dementia with Lewy bodies (DLB), features substantial clinical and pathological overlap with Alzheimer's disease (AD). The identification of biomarkers unique to LBD pathophysiology could meaningfully advance its diagnosis, monitoring, and treatment. Using quantitative mass spectrometry (MS), we measured over 9,000 proteins across 138 dorsolateral prefrontal cortex (DLPFC) tissues from a University of Pennsylvania autopsy collection comprising control, Parkinson's disease (PD), PDD, and DLB diagnoses. We then analyzed co-expression network protein alterations in those with LBD, validated these disease signatures in two independent LBD datasets, and compared these findings to those observed in network analyses of AD cases. The LBD network revealed numerous groups or "modules" of co-expressed proteins significantly altered in PDD and DLB, representing synaptic, metabolic, and inflammatory pathophysiology. A comparison of validated LBD signatures to those of AD identified distinct differences between the two diseases. Notably, synuclein-associated presynaptic modules were elevated in LBD but decreased in AD relative to controls. We also found that glial-associated matrisome signatures consistently elevated in AD were more variably altered in LBD, ultimately stratifying those LBD cases with low versus high burdens of concurrent beta-amyloid deposition. In conclusion, unbiased network proteomic analysis revealed diverse pathophysiological changes in the LBD frontal cortex distinct from alterations in AD. These results highlight the LBD brain network proteome as a promising source of biomarkers that could enhance clinical recognition and management.

Proteomic Changes in the Human Cerebrovasculature in Alzheimer's Disease and Related Tauopathies Linked to Peripheral Biomarkers in Plasma and Cerebrospinal Fluid

Dysfunction of the neurovascular unit stands as a significant pathological hallmark of Alzheimer's disease (AD) and age-related neurodegenerative diseases. Nevertheless, detecting vascular changes in the brain within bulk tissues has proven challenging, limiting our ability to characterize proteomic alterations from less abundant cell types. To address this challenge, we conducted quantitative proteomic analyses on both bulk brain tissues and cerebrovascular-enriched fractions from the same individuals, encompassing cognitively unimpaired control, progressive supranuclear palsy (PSP), and AD cases. Protein co-expression network analysis identified modules unique to the cerebrovascular fractions, specifically enriched with pericytes, endothelial cells, and smooth muscle cells. Many of these modules also exhibited significant correlations with amyloid plaques, cerebral amyloid angiopathy (CAA), and/or tau pathology in the brain. Notably, the protein products within AD genetic risk loci were found concentrated within modules unique to the vascular fractions, consistent with a role of cerebrovascular deficits in the etiology of AD. To prioritize peripheral AD biomarkers associated with vascular dysfunction, we assessed the overlap between differentially abundant proteins in AD cerebrospinal fluid (CSF) and plasma with a vascular-enriched network modules in the brain. This analysis highlighted matrisome proteins, SMOC1 and SMOC2, as being increased in CSF, plasma, and brain. Immunohistochemical analysis revealed SMOC1 deposition in both parenchymal plaques and CAA in the AD brain, whereas SMOC2 was predominantly localized to CAA. Collectively, these findings significantly enhance our understanding of the involvement of cerebrovascular abnormalities in AD, shedding light on potential biomarkers and molecular pathways associated with CAA and vascular dysfunction in neurodegenerative diseases.

Proteomic Network Analysis of Alzheimer's Disease Cerebrospinal Fluid Reveals Alterations Associated with APOE ε4 Genotype and Atomoxetine Treatment

Alzheimer's disease (AD) is currently defined at the research level by the aggregation of amyloid-β (Aβ) and tau proteins in brain. While biofluid biomarkers are available to measure Aβ and tau pathology, few biomarkers are available to measure the complex pathophysiology that is associated with these two cardinal neuropathologies. Here we describe the proteomic landscape of cerebrospinal fluid (CSF) changes associated with Aβ and tau pathology in 300 individuals as assessed by two different proteomic technologies-tandem mass tag (TMT) mass spectrometry and SomaScan. Harmonization and integration of both data types allowed for generation of a robust protein co-expression network consisting of 34 modules derived from 5242 protein measurements, including disease-relevant modules associated with autophagy, ubiquitination, endocytosis, and glycolysis. Three modules strongly associated with the apolipoprotein E ε4 (APOE ε4) AD risk genotype mapped to oxidant detoxification, mitogen associated protein kinase (MAPK) signaling, neddylation, and mitochondrial biology, and overlapped with a previously described lipoprotein module in serum. Neddylation and oxidant detoxification/MAPK signaling modules had a negative association with APOE ε4 whereas the mitochondrion module had a positive association with APOE ε4. The directions of association were consistent between CSF and blood in two independent longitudinal cohorts, and altered levels of all three modules in blood were associated with dementia over 20 years prior to diagnosis. Dual-proteomic platform analysis of CSF samples from an AD phase 2 clinical trial of atomoxetine (ATX) demonstrated that abnormal elevations in the glycolysis CSF module-the network module most strongly correlated to cognitive function-were reduced by ATX treatment. Individuals who had more severe glycolytic changes at baseline responded better to ATX. Clustering of individuals based on their CSF proteomic network profiles revealed ten groups that did not cleanly stratify by Aβ and tau status, underscoring the heterogeneity of pathological changes not fully reflected by Aβ and tau. AD biofluid proteomics holds promise for the development of biomarkers that reflect diverse pathologies for use in clinical trials and precision medicine.

Unbiased classification of the elderly human brain proteome resolves distinct clinical and pathophysiological subtypes of cognitive impairment

Cognitive impairment in the elderly features complex molecular pathophysiology extending beyond the hallmark pathologies of traditional disease classification. Molecular subtyping using large-scale -omic strategies can help resolve this biological heterogeneity. Using quantitative mass spectrometry, we measured ∼8000 proteins across >600 dorsolateral prefrontal cortex tissues with clinical diagnoses of no cognitive impairment (NCI), mild cognitive impairment (MCI), and Alzheimer's disease (AD) dementia. Unbiased classification of MCI and AD cases based on individual proteomic profiles resolved three classes with expression differences across numerous cell types and biological ontologies. Two classes displayed molecular signatures atypical of AD neurodegeneration, such as elevated synaptic and decreased inflammatory markers. In one class, these atypical proteomic features were associated with clinical and pathological hallmarks of cognitive resilience. We were able to replicate these classes and their clinicopathological phenotypes across two additional tissue cohorts. These results promise to better define the molecular heterogeneity of cognitive impairment and meaningfully impact its diagnostic and therapeutic precision.

Robust quantitative susceptibility mapping via approximate message passing with parameter estimation

PURPOSE

For quantitative susceptibility mapping (QSM), the lack of ground-truth in clinical settings makes it challenging to determine suitable parameters for the dipole inversion. We propose a probabilistic Bayesian approach for QSM with built-in parameter estimation, and incorporate the nonlinear formulation of the dipole inversion to achieve a robust recovery of the susceptibility maps.

THEORY

From a Bayesian perspective, the image wavelet coefficients are approximately sparse and modeled by the Laplace distribution. The measurement noise is modeled by a Gaussian-mixture distribution with two components, where the second component is used to model the noise outliers. Through probabilistic inference, the susceptibility map and distribution parameters can be jointly recovered using approximate message passing (AMP).

METHODS

We compare our proposed AMP with built-in parameter estimation (AMP-PE) to the state-of-the-art L1-QSM, FANSI, and MEDI approaches on the simulated and in vivo datasets, and perform experiments to explore the optimal settings of AMP-PE. Reproducible code is available at: https://github.com/EmoryCN2L/QSM_AMP_PE.

RESULTS

On the simulated Sim2Snr1 dataset, AMP-PE achieved the lowest NRMSE, deviation from calcification moment and the highest SSIM, while MEDI achieved the lowest high-frequency error norm. On the in vivo datasets, AMP-PE is robust and successfully recovers the susceptibility maps using the estimated parameters, whereas L1-QSM, FANSI and MEDI typically require additional visual fine-tuning to select or double-check working parameters.

CONCLUSION

AMP-PE provides automatic and adaptive parameter estimation for QSM and avoids the subjectivity from the visual fine-tuning step, making it an excellent choice for the clinical setting.

Blood-based biomarkers in Alzheimer's disease: Future directions for implementation

INTRODUCTION

Disease-modifying therapies (DMTs) for Alzheimer's disease (AD) will increase diagnostic demand. A non-invasive blood-based biomarker (BBBM) test for detection of amyloid-β pathology may reduce diagnostic barriers and facilitate DMT initiation.

OBJECTIVE

To explore heterogeneity in AD care pathways and potential role of BBBM tests.

METHODS

Survey of 213 healthcare professionals/payers in US/China/UK/Germany/Spain/France and two advisory boards (US/Europe).

RESULTS

Current diagnostic pathways are heterogeneous, meaning many AD patients are missed while low-risk patients undergo unnecessary procedures. Confirmatory amyloid testing (cerebrospinal fluid biomarkers/positron emission tomography) is utilized in few patients, resulting in diagnostic/treatment delays. A high negative-predictive-value test could streamline the diagnostic pathway by reducing unnecessary procedures in low-risk patients; supporting confirmatory testing where needed. Imminent approval of DMTs will increase need for fast and reliable AD diagnostic tests.

DISCUSSION

An easy-to-use, accurate, non-invasive BBBM test for amyloid pathology could guide diagnostic procedures or referral, streamlining early diagnosis and DMT initiation.

Highlights

This study explored AD care pathways and how BBBM may meet diagnostic demandsCurrent diagnostic pathways are heterogeneous, with country and setting variationsMany AD patients are missed, while low-risk patients undergo unnecessary proceduresAn easy-to-use, accurate, non-invasive BBBM test for amyloid pathology is neededThis test could streamline early diagnosis of amyloid pathology and DMT initiation.

A protein panel in cerebrospinal fluid for diagnostic and predictive assessment of Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disease with heterogenous pathophysiological changes that develop years before the onset of clinical symptoms. These preclinical changes have generated considerable interest in identifying markers for the pathophysiological mechanisms linked to AD and AD-related disorders (ADRD). On the basis of our prior work integrating cerebrospinal fluid (CSF) and brain proteome networks, we developed a reliable and high-throughput mass spectrometry-selected reaction monitoring assay that targets 48 key proteins altered in CSF. To test the diagnostic utility of these proteins and compare them with existing AD biomarkers, CSF collected at baseline visits was assayed from 706 participants recruited from the Alzheimer's Disease Neuroimaging Initiative. We found that the targeted CSF panel of 48 proteins (CSF 48 panel) performed at least as well as existing AD CSF biomarkers (Aβ42, tTau, and pTau181) for predicting clinical diagnosis, FDG PET, hippocampal volume, and measures of cognitive and dementia severity. In addition, for each of those outcomes, the CSF 48 panel plus the existing AD CSF biomarkers significantly improved diagnostic performance. Furthermore, the CSF 48 panel plus existing AD CSF biomarkers significantly improved predictions for changes in FDG PET, hippocampal volume, and measures of cognitive decline and dementia severity compared with either measure alone. A potential reason for these improvements is that the CSF 48 panel reflects a range of altered biology observed in AD/ADRD. In conclusion, we show that the CSF 48 panel complements existing AD CSF biomarkers to improve diagnosis and predict future cognitive decline and dementia severity.

Integration of plasma and CSF metabolomics with CSF proteomic reveals novel associations between lipid mediators and central nervous system vascular and energy metabolism

Integration of the omics data, including metabolomics and proteomics, provides a unique opportunity to search for new associations within metabolic disorders, including Alzheimer's disease. Using metabolomics, we have previously profiled oxylipins, endocannabinoids, bile acids, and steroids in 293 CSF and 202 matched plasma samples from AD cases and healthy controls and identified both central and peripheral markers of AD pathology within inflammation-regulating cytochrome p450/soluble epoxide hydrolase pathway. Additionally, using proteomics, we have identified five cerebrospinal fluid protein panels, involved in the regulation of energy metabolism, vasculature, myelin/oligodendrocyte, glia/inflammation, and synapses/neurons, affected in AD, and reflective of AD-related changes in the brain. In the current manuscript, using metabolomics-proteomics data integration, we describe new associations between peripheral and central lipid mediators, with the above-described CSF protein panels. Particularly strong associations were observed between cytochrome p450/soluble epoxide hydrolase metabolites, bile acids, and proteins involved in glycolysis, blood coagulation, and vascular inflammation and the regulators of extracellular matrix. Those metabolic associations were not observed at the gene-co-expression level in the central nervous system. In summary, this manuscript provides new information regarding Alzheimer's disease, linking both central and peripheral metabolism, and illustrates the necessity for the "omics" data integration to uncover associations beyond gene co-expression.

Cerebrospinal fluid proteomics define the natural history of autosomal dominant Alzheimer's disease

Alzheimer's disease (AD) pathology develops many years before the onset of cognitive symptoms. Two pathological processes-aggregation of the amyloid-β (Aβ) peptide into plaques and the microtubule protein tau into neurofibrillary tangles (NFTs)-are hallmarks of the disease. However, other pathological brain processes are thought to be key disease mediators of Aβ plaque and NFT pathology. How these additional pathologies evolve over the course of the disease is currently unknown. Here we show that proteomic measurements in autosomal dominant AD cerebrospinal fluid (CSF) linked to brain protein coexpression can be used to characterize the evolution of AD pathology over a timescale spanning six decades. SMOC1 and SPON1 proteins associated with Aβ plaques were elevated in AD CSF nearly 30 years before the onset of symptoms, followed by changes in synaptic proteins, metabolic proteins, axonal proteins, inflammatory proteins and finally decreases in neurosecretory proteins. The proteome discriminated mutation carriers from noncarriers before symptom onset as well or better than Aβ and tau measures. Our results highlight the multifaceted landscape of AD pathophysiology and its temporal evolution. Such knowledge will be critical for developing precision therapeutic interventions and biomarkers for AD beyond those associated with Aβ and tau.

Quantitative proteomics of cerebrospinal fluid from African Americans and Caucasians reveals shared and divergent changes in Alzheimer's disease

BACKGROUND

Despite being twice as likely to get Alzheimer's disease (AD), African Americans have been grossly underrepresented in AD research. While emerging evidence indicates that African Americans with AD have lower cerebrospinal fluid (CSF) levels of Tau compared to Caucasians, other differences in AD CSF biomarkers have not been fully elucidated. Here, we performed unbiased proteomic profiling of CSF from African Americans and Caucasians with and without AD to identify both common and divergent AD CSF biomarkers.

METHODS

Multiplex tandem mass tag-based mass spectrometry (TMT-MS) quantified 1,840 proteins from 105 control and 98 AD patients of which 100 identified as Caucasian while 103 identified as African American. We used differential protein expression and co-expression approaches to assess how changes in the CSF proteome are related to race and AD. Co-expression network analysis organized the CSF proteome into 14 modules associated with brain cell-types and biological pathways. A targeted mass spectrometry method, selected reaction monitoring (SRM), with heavy labeled internal standards was used to measure a panel of CSF module proteins across a subset of African Americans and Caucasians with or without AD. A receiver operating characteristic (ROC) curve analysis assessed the performance of each protein biomarker in differentiating controls and AD by race.

RESULTS

Consistent with previous findings, the increase of Tau levels in AD was greater in Caucasians than in African Americans by both immunoassay and TMT-MS measurements. CSF modules which included 14-3-3 proteins (YWHAZ and YWHAG) demonstrated equivalent disease-related elevations in both African Americans and Caucasians with AD, whereas other modules demonstrated more profound disease changes within race. Modules enriched with proteins involved with glycolysis and neuronal/cytoskeletal proteins, including Tau, were more increased in Caucasians than in African Americans with AD. In contrast, a module enriched with synaptic proteins including VGF, SCG2, and NPTX2 was significantly lower in African Americans than Caucasians with AD. Following SRM and ROC analysis, VGF, SCG2, and NPTX2 were significantly better at classifying African Americans than Caucasians with AD.

CONCLUSIONS

Our findings provide insight into additional protein biomarkers and pathways reflecting underlying brain pathology that are shared or differ by race.

Multi-ancestry genome-wide meta-analysis of 56,241 individuals identifies LRRC4C, LHX5-AS1 and nominates ancestry-specific loci PTPRK , GRB14 , and KIAA0825 as novel risk loci for Alzheimer's disease: the Alzheimer's Disease Genetics Consortium

Limited ancestral diversity has impaired our ability to detect risk variants more prevalent in non-European ancestry groups in genome-wide association studies (GWAS). We constructed and analyzed a multi-ancestry GWAS dataset in the Alzheimer's Disease (AD) Genetics Consortium (ADGC) to test for novel shared and ancestry-specific AD susceptibility loci and evaluate underlying genetic architecture in 37,382 non-Hispanic White (NHW), 6,728 African American, 8,899 Hispanic (HIS), and 3,232 East Asian individuals, performing within-ancestry fixed-effects meta-analysis followed by a cross-ancestry random-effects meta-analysis. We identified 13 loci with cross-ancestry associations including known loci at/near CR1 , BIN1 , TREM2 , CD2AP , PTK2B , CLU , SHARPIN , MS4A6A , PICALM , ABCA7 , APOE and two novel loci not previously reported at 11p12 ( LRRC4C ) and 12q24.13 ( LHX5-AS1 ). Reflecting the power of diverse ancestry in GWAS, we observed the SHARPIN locus using 7.1% the sample size of the original discovering single-ancestry GWAS (n=788,989). We additionally identified three GWS ancestry-specific loci at/near ( PTPRK ( P =2.4×10 -8 ) and GRB14 ( P =1.7×10 -8 ) in HIS), and KIAA0825 ( P =2.9×10 -8 in NHW). Pathway analysis implicated multiple amyloid regulation pathways (strongest with P adjusted =1.6×10 -4 ) and the classical complement pathway ( P adjusted =1.3×10 -3 ). Genes at/near our novel loci have known roles in neuronal development ( LRRC4C, LHX5-AS1 , and PTPRK ) and insulin receptor activity regulation ( GRB14 ). These findings provide compelling support for using traditionally-underrepresented populations for gene discovery, even with smaller sample sizes.

Differential DNA Methylation in the Brain as Potential Mediator of the Association between Traffic-related PM2.5 and Neuropathology Markers of Alzheimer's Disease

INTRODUCTION

Growing evidence indicates fine particulate matter (PM2.5) as risk factor for Alzheimer's' disease (AD), but the underlying mechanisms have been insufficiently investigated. We hypothesized differential DNA methylation (DNAm) in brain tissue as potential mediator of this association.

METHODS

We assessed genome-wide DNAm (Illumina EPIC BeadChips) in prefrontal cortex tissue and three AD-related neuropathological markers (Braak stage, CERAD, ABC score) for 159 donors, and estimated donors' residential traffic-related PM2.5 exposure 1, 3 and 5 years prior to death. We used a combination of the Meet-in-the-Middle approach, high-dimensional mediation analysis, and causal mediation analysis to identify potential mediating CpGs.

RESULTS

PM2.5 was significantly associated with differential DNAm at cg25433380 and cg10495669. Twenty-six CpG sites were identified as mediators of the association between PM2.5 exposure and neuropathology markers, several located in genes related to neuroinflammation.

DISCUSSION

Our findings suggest differential DNAm related to neuroinflammation mediates the association between traffic-related PM2.5 and AD.

Telehealth equivalence of the Montreal cognitive assessment (MoCA): Results from the Emory healthy brain study (EHBS)

BACKGROUND

We investigated potential differences between in-person cognitive testing and video telehealth administration of the Montreal Cognitive Assessment (MoCA). In addition to the MoCA, the Patient Health Questionnaire-8 (PHQ-8) and Generalized Anxiety Disorder-7 (GAD-7) were administered.

METHODS

MoCA scores from participants in the Emory Health Brain Study (EHBS) were contrasted based upon whether they were administered the MoCA in the standard face-to-face (F2F) assessment setting (n = 1205) or using a video telehealth administration (n = 491). All EHBS participants were self-reported to be cognitively normal.

RESULTS

MoCA scores did not differ across administration method (F2F MoCA = 26.6, SD = 2.4; telehealth MoCA = 26.5, SD = 2.4). The 95% confidence interval for difference in administration was small (CI = -0.16 to 0.34). When examining MoCA domain scores, administration differences were either associated with no statistically significant effect, or if present due to large sample sizes, were associated with small effects and differences <0.5 point. Telehealth patients reported slightly lower PHQ-8 scores (F2F PHQ-8 = 2.0, SD = 2.5; telehealth PHQ-8 = 1.6, SD = 2.1), although these scores are well within the normal range. No group difference in GAD-7 scores was present (F2F GAD-7 = 1.4, SD = 2.4; telehealth PHQ-8 = 1.4, SD = 2.4).

DISCUSSION

This report with its large sample size and between subject cohort provides complementary evidence to smaller test-retest studies, further supporting equivalence of MoCA telehealth testing to F2F MoCA administration. These findings provide additional reassurance that administration mode does not introduce systematic performance differences for MoCA test administration, thereby permitting telehealth MoCA testing to be applied confidently for both clinical and research applications.

Disentangling visual exploration differences in cognitive impairment

Objective

Compared to individuals without cognitive impairment (CI), those with CI exhibit differences in both basic oculomotor functions and complex viewing behaviors. However, the characteristics of the differences and how those differences relate to various cognitive functions have not been widely explored. In this work we aimed to quantify those differences and assess general cognitive impairment and specific cognitive functions.

Methods

A validated passive viewing memory test with eyetracking was administered to 348 healthy controls and CI individuals. Spatial, temporal, semantic, and other composite features were extracted from the estimated eye-gaze locations on the corresponding pictures displayed during the test. These features were then used to characterize viewing patterns, classify cognitive impairment, and estimate scores in various neuropsychological tests using machine learning.

Results

Statistically significant differences in spatial, spatiotemporal, and semantic features were found between healthy controls and individuals with CI. CI group spent more time gazing at the center of the image, looked at more regions of interest (ROI), transitioned less often between ROI yet in a more unpredictable manner, and had different semantic preferences. A combination of these features achieved an area under the receiver-operator curve of 0.78 in differentiating CI individuals from controls. Statistically significant correlations were identified between actual and estimated MoCA scores and other neuropsychological tests.

Conclusion

Evaluating visual exploration behaviors provided quantitative and systematic evidence of differences in CI individuals, leading to an improved approach for passive cognitive impairment screening.

Significance

The proposed passive, accessible, and scalable approach could help with earlier detection and a better understanding of cognitive impairment.

Quantitative Mass Spectrometry Analysis of Cerebrospinal Fluid Protein Biomarkers in Alzheimer's Disease

Alzheimer's disease (AD) is the most common form of dementia, with cerebrospinal fluid (CSF) β-amyloid (Aβ), total Tau, and phosphorylated Tau (pTau) providing the most sensitive and specific biomarkers for diagnosis. However, these diagnostic biomarkers do not reflect the complex changes in AD brain beyond amyloid (A) and Tau (T) pathologies. Here, we report a selected reaction monitoring mass spectrometry (SRM-MS) method with isotopically labeled standards for relative protein quantification in CSF. Biomarker positive (AT+) and negative (AT-) CSF pools were used as quality controls (QCs) to assess assay precision. We detected 62 peptides (51 proteins) with an average coefficient of variation (CV) of ~13% across 30 QCs and 133 controls (cognitively normal, AT-), 127 asymptomatic (cognitively normal, AT+) and 130 symptomatic AD (cognitively impaired, AT+). Proteins that could distinguish AT+ from AT- individuals included SMOC1, GDA, 14-3-3 proteins, and those involved in glycolysis. Proteins that could distinguish cognitive impairment were mainly neuronal proteins (VGF, NPTX2, NPTXR, and SCG2). This demonstrates the utility of SRM-MS to quantify CSF protein biomarkers across stages of AD.

The Rey Auditory Verbal Learning Test: Cross-validation of Mayo Normative Studies (MNS) demographically corrected norms with confidence interval estimates

OBJECTIVE

The Mayo Normative Studies (MNS) represents a robust dataset that provides demographically corrected norms for the Rey Auditory Verbal Learning Test. We report MNS application to an independent cohort to evaluate whether MNS norms accurately adjust for age, sex, and education differences in subjects from a different geographic region of the country. As secondary goals, we examined item-level patterns, recognition benefit compared to delayed free recall, and derived Auditory Verbal Learning Test (AVLT) confidence intervals (CIs) to facilitate clinical performance characterization.

METHOD

Participants from the Emory Healthy Brain Study (463 women, 200 men) who were administered the AVLT were analyzed to demonstrate expected demographic group differences. AVLT scores were transformed using MNS normative correction to characterize the success of MNS demographic adjustment.

RESULTS

Expected demographic effects were observed across all primary raw AVLT scores. Depending on sample size, MNS normative adjustment either eliminated or minimized all observed statistically significant AVLT differences. Estimated CIs yielded broad CI ranges exceeding the standard deviation of each measure. The recognition performance benefit across age ranged from 2.7 words (SD = 2.3) in the 50-54-year-old group to 4.7 words (SD = 2.7) in the 70-75-year-old group.

CONCLUSIONS

These findings demonstrate generalizability of MNS normative correction to an independent sample from a different geographic region, with demographic adjusted performance differences close to overall performance levels near the expected value of T = 50. A large recognition performance benefit is commonly observed in the normal aging process and by itself does not necessarily suggest a pathological retrieval deficit.

Fine particulate air pollution and neuropathology markers of Alzheimer's disease in donors with and without APOE ε4 alleles - results from an autopsy cohort

Introduction

Higher fine particulate matter (PM2.5) exposure has been found to be associated with Alzheimer's disease (AD). PM2.5 has been hypothesized to cause inflammation and oxidative stress in the brain, contributing to neuropathology. A major genetic risk factor of AD, the apolipoprotein E (APOE) gene, has also been hypothesized to modify the association between PM2.5 and AD. However, little prior research exisits to support these hypotheses. Therefore, this paper aims to investigate the association between traffic-related PM2.5 and AD hallmark pathology, including effect modification by APOE genotype, in an autopsy cohort.

Methods

Brain tissue donors enrolled in the Emory Goizueta Alzheimer's Disease Research Center (ADRC) who died before 2020 (n=224) were assessed for AD pathology including Braak Stage, Consortium to Establish a Registry for AD (CERAD) score, and the combined AD neuropathologic change (ABC score). Traffic-related PM2.5 concentrations were modeled for the metro-Atlanta area during 2002-2019 with a spatial resolution of 200-250m. One-, 3-, and 5-year average PM2.5 concentrations prior to death were matched to participants home address. We assessed the association between traffic-related PM2.5 and AD hallmark pathology, as well as effect modification by APOE genotype, using adjusted ordinal logistic regression models.

Results

Traffic-related PM2.5 was significantly associated with CERAD score for the 1-year exposure window (OR: 1.92; 95% CI: 1.12, 3.30), and the 3-year exposure window (OR: 1.87; 95%-CI: 1.01, 3.17). PM2.5 had harmful, but non-significant associations on Braak Stage and ABC score. The strongest associations between PM2.5 and neuropathology markers were among those without APOE ε4 alleles (e.g., for CERAD and 1-year exposure window, OR: 2.31; 95% CI: 1.36, 3.94), though interaction between PM2.5 and APOE genotype was not statistically significant.

Conclusions

Our study found traffic-related PM2.5 exposure was associated with CERAD score in an autopsy cohort, contributing to epidemiologic evidence that PM2.5 affects Aβ deposition in the brain. This association was particularly strong among donors without APOE ε4 alleles. Future studies should further investigate the biological mechanisms behind this assocation.

Predictors of Cognitive Decline in Healthy Middle-Aged Individuals with Asymptomatic Alzheimer's Disease

Alzheimer's disease (AD) progresses through a lengthy asymptomatic period during which pathological changes accumulate prior to development of clinical symptoms. As disease-modifying treatments are developed, tools to stratify risk of clinical disease will be required to guide their use. In this study, we examine the relationship of AD biomarkers in healthy middle-aged individuals to health history, family history, and neuropsychological measures and identify cerebrospinal fluid (CSF) biomarkers to stratify risk of progression from asymptomatic to symptomatic AD. CSF from cognitively normal (CN) individuals (N=1149) in the Emory Healthy Brain Study were assayed for Aβ42, total Tau (tTau), and phospho181-Tau (pTau), and a subset of 134 cognitively normal, but biomarker-positive, individuals were identified with asymptomatic AD (AsymAD) based on a locally-determined cutoff value for ratio of tTau to Aβ42. These AsymAD cases were matched for demographic features with 134 biomarker-negative controls (CN/BM-) and compared for differences in medical comorbidities and family history. Dyslipidemia emerged as a distinguishing feature between AsymAD and CN/BM-groups with significant association with personal and family history of dyslipidemia. A weaker relationship was seen with diabetes, but there was no association with hypertension. Examination of the full cohort by median regression revealed a significant relationship of CSF Aβ42 (but not tTau or pTau) with dyslipidemia and diabetes. On neuropsychological tests, CSF Aβ42 was not correlated with performance on any measures, but tTau and pTau were strongly correlated with visuospatial perception and visual episodic memory. In addition to traditional CSF AD biomarkers, a panel of AD biomarker peptides derived from integrating brain and CSF proteomes were evaluated using machine learning strategies to identify a set of 8 peptides that accurately classified CN/BM- and symptomatic AD CSF samples with AUC of 0.982. Using these 8 peptides in a low dimensional t-distributed Stochastic Neighbor Embedding analysis and k-Nearest Neighbor (k=5) algorithm, AsymAD cases were stratified into "Control-like" and "AD-like" subgroups based on their proximity to CN/BM- or AD CSF profiles. Independent analysis of these cases using a Joint Mutual Information algorithm selected a set of 5 peptides with 81% accuracy in stratifying cases into AD-like and Control-like subgroups. Performance of both sets of peptides was evaluated and validated in an independent data set from the Alzheimer's Disease Neuroimaging Initiative. Based on our findings, we conclude that there is an important role of lipid metabolism in asymptomatic stages of AD. Visuospatial perception and visual episodic memory may be more sensitive than language-based abilities to earliest stages of cognitive decline in AD. Finally, candidate CSF peptides show promise as next generation biomarkers for predicting progression from asymptomatic to symptomatic stages of AD.

Machine Learning Selection of Most Predictive Brain Proteins Suggests Role of Sugar Metabolism in Alzheimer's Disease

BACKGROUND

The complex and not yet fully understood etiology of Alzheimer's disease (AD) shows important proteopathic signs which are unlikely to be linked to a single protein. However, protein subsets from deep proteomic datasets can be useful in stratifying patient risk, identifying stage dependent disease markers, and suggesting possible disease mechanisms.

OBJECTIVE

The objective was to identify protein subsets that best classify subjects into control, asymptomatic Alzheimer's disease (AsymAD), and AD.

METHODS

Data comprised 6 cohorts; 620 subjects; 3,334 proteins. Brain tissue-derived predictive protein subsets for classifying AD, AsymAD, or control were identified and validated with label-free quantification and machine learning.

RESULTS

A 29-protein subset accurately classified AD (AUC = 0.94). However, an 88-protein subset best predicted AsymAD (AUC = 0.92) or Control (AUC = 0.92) from AD (AUC = 0.98). AD versus Control: APP, DHX15, NRXN1, PBXIP1, RABEP1, STOM, and VGF. AD versus AsymAD: ALDH1A1, BDH2, C4A, FABP7, GABBR2, GNAI3, PBXIP1, and PRKAR1B. AsymAD versus Control: APP, C4A, DMXL1, EXOC2, PITPNB, RABEP1, and VGF. Additional predictors: DNAJA3, PTBP2, SLC30A9, VAT1L, CROCC, PNP, SNCB, ENPP6, HAPLN2, PSMD4, and CMAS.

CONCLUSION

Biomarkers were dynamically separable across disease stages. Predictive proteins were significantly enriched to sugar metabolism.

Multi-platform proteomic analysis of Alzheimer’s disease cerebrospinal fluid and plasma reveals network biomarkers associated with proteostasis and the matrisome

Robust and accessible biomarkers that can capture the heterogeneity of Alzheimer’s disease and its diverse pathological processes are urgently needed. Here, we undertook an investigation of Alzheimer’s disease cerebrospinal fluid (CSF) and plasma from the same subjects (n=18 control, n=18 AD) using three different proteomic platforms—SomaLogic SomaScan, Olink proximity extension assay, and tandem mass tag-based mass spectrometry—to assess which protein markers in these two biofluids may serve as reliable biomarkers of AD pathophysiology observed from unbiased brain proteomics studies. Median correlation of overlapping protein measurements across platforms in CSF (r~0.7) and plasma (r~0.6) was good, with more variability in plasma. The SomaScan technology provided the most measurements in plasma. Surprisingly, many proteins altered in AD CSF were found to be altered in the opposite direction in plasma, including important members of AD brain co-expression modules. An exception was SMOC1, a key member of the brain matrisome module associated with amyloid-β deposition in AD, which was found to be elevated in both CSF and plasma. Protein co-expression analysis on greater than 7000 protein measurements in CSF and 9500 protein measurements in plasma across all proteomic platforms revealed strong changes in modules related to autophagy, ubiquitination, and sugar metabolism in CSF, and endocytosis and the matrisome in plasma. Cross-platform and cross-biofluid proteomics represents a promising approach for AD biomarker development.

CSF proteome profiling across the Alzheimer's disease spectrum reflects the multifactorial nature of the disease and identifies specific biomarker panels

Development of disease-modifying therapies against Alzheimer's disease (AD) requires biomarkers reflecting the diverse pathological pathways specific for AD. We measured 665 proteins in 797 cerebrospinal fluid (CSF) samples from patients with mild cognitive impairment with abnormal amyloid (MCI(Aβ+): n = 50), AD-dementia (n = 230), non-AD dementias (n = 322) and cognitively unimpaired controls (n = 195) using proximity ligation-based immunoassays. Here we identified >100 CSF proteins dysregulated in MCI(Aβ+) or AD compared to controls or non-AD dementias. Proteins dysregulated in MCI(Aβ+) were primarily related to protein catabolism, energy metabolism and oxidative stress, whereas those specifically dysregulated in AD dementia were related to cell remodeling, vascular function and immune system. Classification modeling unveiled biomarker panels discriminating clinical groups with high accuracies (area under the curve (AUC): 0.85-0.99), which were translated into custom multiplex assays and validated in external and independent cohorts (AUC: 0.8-0.99). Overall, this study provides novel pathophysiological leads delineating the multifactorial nature of AD and potential biomarker tools for diagnostic settings or clinical trials.

Examination of the reliability and feasibility of two smartphone applications to assess executive functioning in racially diverse older adults

Inclusion of Black participants in clinical research is a national priority. Mobile applications and remote data collection may increase study access for diverse populations. This study examined the reliability and feasibility of two mobile smartphone application-based cognitive measures in a diverse middle aged and older adult sample. Black (n = 44; Mage = 59.93) and non-Hispanic white (NHW; n = 50; Mage = 61.06) participants completed traditional paper-based neuropsychological testing and two app-based measures, Arrows and Number Match. Intraclass correlations demonstrated poor to moderate reliability (range: .417-.569) between performance on the app-based versions and performance on the traditional versions. Performance score differences by racial group were not statistically significant. Both Black and NHW participants rated the app-based measures as feasible and acceptable, though Black participants endorsed a stronger likelihood of future use. These findings add to the growing literature on remote cognitive testing .

Safety and biomarker effects of candesartan in non-hypertensive adults with prodromal Alzheimer's disease

Observational studies suggest that angiotensin receptor blockers in hypertensive adults are associated with lower post-mortem indicators of Alzheimer's disease pathology. Candesartan, an angiotensin receptor blocker, has a positive cognitive effect in mild cognitive impairment with hypertension. However, its safety and effects in non-hypertensive individuals with Alzheimer's disease are unclear. This is the first double-blind randomized placebo-controlled trial aimed to assess safety and effects of 1-year therapy of candesartan on biomarkers and clinical indicators of Alzheimer's disease in non-hypertensive individuals with biomarker-confirmed prodromal Alzheimer's disease. Seventy-seven non-hypertensive participants 50 years or older (mean age: 68.1 years; 62% women; 20% African American) with mild cognitive impairment and biomarker confirmed Alzheimer's disease were randomized to escalating doses of once daily oral candesartan (up to 32 mg) or matched placebo. Main outcomes included safety and tolerability of candesartan, cerebrospinal fluid biomarkers (amyloid-β42, amyloid-β40, total tau and phospho-tau). Additional exploratory outcomes included PET imaging (Pittsburgh Compound-B (¹¹C-PiB) and ¹⁸F-flortaucipir), brain MRI (structural and connectivity measures) and cognitive functioning. Analyses used intention-to-treat approach with group comparisons of safety measures using Chi-square test, and repeated measures mixed effects models were used to assess candesartan effects on main and exploratory outcomes (ClinicalTrials.gov, NCT02646982). Candesartan was found to be safe with no significant difference in safety measures: symptoms of hypotension, renal failure or hyperkalemia. Candesartan was also found to be associated with increases in cerebrospinal fluid Aβ40 (between-group mean difference: 1211.95 pg/ml, 95% confidence interval: 313.27, 2110.63) and Aβ42 (49.51 pg/ml, 95% confidence interval: -98.05, -0.98) reflecting lower brain amyloid accumulation. Candesartan was associated with decreased ¹¹C-PiB in the parahippocampal region (-0.1104, 95% confidence interval: -0.19, -0.029) which remained significant after false discovery rate correction, and with an increase in functional network connectivity in the subcortical networks. Candesartan was further associated with improved executive function (Trail Making Test Part B) performance (-11.41 s, 95% confidence interval: -11.94, -10.89) and trended for an improved global cognitive functioning reflected by a composite cognitive score (0.002, 95% confidence interval: -0.0002, 0.005). We did not observe significant effects on tau levels, hippocampal volume or other cognitive measures (memory or clinical dementia rating scale-sum of boxes). In conclusion, among non-hypertensive prodromal Alzheimer's disease, candesartan is safe and likely decreases brain amyloid biomarkers, enhances subcortical brain connectivity and has favourable cognitive effects. These findings suggest that candesartan may have an important therapeutic role in Alzheimer's disease, and warrant further investigation given the lack of clear treatment options for this devastating illness.

A probabilistic Bayesian approach to recover R2*$$ {R}_{2\ast } $$ map and phase images for quantitative susceptibility mapping

PURPOSE

Undersampling is used to reduce the scan time for high-resolution three-dimensional magnetic resonance imaging. In order to achieve better image quality and avoid manual parameter tuning, we propose a probabilistic Bayesian approach to recover R 2 ∗ $$ {R}_2^{\ast } $$ map and phase images for quantitative susceptibility mapping (QSM), while allowing automatic parameter estimation from undersampled data.

THEORY

Sparse prior on the wavelet coefficients of images is interpreted from a Bayesian perspective as sparsity-promoting distribution. A novel nonlinear approximate message passing (AMP) framework that incorporates a mono-exponential decay model is proposed. The parameters are treated as unknown variables and jointly estimated with image wavelet coefficients.

METHODS

Undersampling takes place in the y-z plane of k-space according to the Poisson-disk pattern. Retrospective undersampling is performed to evaluate the performances of different reconstruction approaches, prospective undersampling is performed to demonstrate the feasibility of undersampling in practice.

RESULTS

The proposed AMP with parameter estimation (AMP-PE) approach successfully recovers R 2 ∗ $$ {R}_2^{\ast } $$ maps and phase images for QSM across various undersampling rates. It is more computationally efficient, and performs better than the state-of-the-art l 1 $$ {l}_1 $$ -norm regularization (L1) approach in general, except a few cases where the L1 approach performs as well as AMP-PE.

CONCLUSION

AMP-PE achieves better performance by drawing information from both the sparse prior and the mono-exponential decay model. It does not require parameter tuning, and works with a clinical, prospective undersampling scheme where parameter tuning is often impossible or difficult due to the lack of ground-truth image.

Neighborhood characteristics as confounders and effect modifiers for the association between air pollution exposure and subjective cognitive functioning

BACKGROUND

Air pollution has been associated with cognitive function in the elderly. Previous studies have not evaluated the simultaneous effect of neighborhood-level socioeconomic status (N-SES), which can be an essential source of bias.

OBJECTIVES

We explored N-SES as a confounder and effect modifier in a cross-sectional study of air pollution and subjective cognitive function.

METHODS

We included 12,058 participants age 50+ years from the Emory Healthy Aging Study in Metro Atlanta using the Cognitive Function Instrument (CFI) score as our outcome, with higher scores representing worse subjective cognitive function. We estimated 9-year average ambient carbon monoxide (CO), nitrogen oxides (NOx), and fine particulate matter (PM2.5) concentrations at residential addresses using a fusion of dispersion and chemical transport models. We collected census-tract level N-SES indicators and created two composite measures via principal component analysis and k-means clustering. Associations between pollutants and CFI and effect modification by N-SES were estimated via linear regression models adjusted for age, education, race and N-SES.

RESULTS

N-SES confounded the association between air pollution and CFI, independent of individual characteristics. We found significant effect modifications by N-SES for the association between air pollution and CFI (p-values<0.001) suggesting that effects of air pollution differ depending on N-SES. Participants living in areas with low N-SES were most vulnerable to air pollution. In the lowest N-SES urban areas, interquartile range (IQR) increases in CO, NOx, and PM2.5 were associated with 5.4% (95%-confidence interval, -0.2,11.3), 4.9% (-0.4,10.4), and 9.8% (2.2,18.0) changes in CFI, respectively. In lowest N-SES suburban areas, IQR increases in CO, NOx, and PM2.5 were associated with higher changes in CFI, namely 13.0% (0.9,26.5), 13.0% (-0.1,27.8), and 17.3% (2.5,34.2), respectively.

DISCUSSION

N-SES is an important confounder and effect modifier in our study. This finding could have implications for studying health effects of air pollution and identifying susceptible populations.

The complex relationship of air pollution and neighborhood socioeconomic status and their association with cognitive decline

BACKGROUND

Air pollution and neighborhood socioeconomic status (nSES) have been shown to affect cognitive decline in older adults. In previous studies, nSES acts as both a confounder and an effect modifier between air pollution and cognitive decline.

OBJECTIVES

This study aims to examine the individual and joint effects of air pollution and nSES on cognitive decline on adults 50 years and older in Metro Atlanta, USA.

METHODS

Perceived memory and cognitive decline was assessed in 11,897 participants aged 50+ years from the Emory Healthy Aging Study (EHAS) using the cognitive function instrument (CFI). Three-year average air pollution concentrations for 12 pollutants and 16 nSES characteristics were matched to participants using census tracts. Individual exposure linear regression and LASSO models explore individual exposure effects. Environmental mixture modeling methods including, self-organizing maps (SOM), Bayesian kernel machine regression (BKMR), and quantile-based G-computation explore joint effects, and effect modification between air pollutants and nSES characteristics on cognitive decline.

RESULTS

Participants living in areas with higher air pollution concentrations and lower nSES experienced higher CFI scores (beta: 0.121; 95 % CI: 0.076, 0.167) compared to participants living in areas with low air pollution and high nSES. Additionally, the BKMR model showed a significant overall mixture effect on cognitive decline, suggesting synergy between air pollution and nSES. These joint effects explain protective effects observed in single-pollutant linear regression models, even after adjustment for confounding by nSES (e.g., an IQR increase in CO was associated with a 0.038-point lower (95 % CI: -0.06, -0.01) CFI score).

DISCUSSION

Observed protective effects of single air pollutants on cognitive decline can be explained by joint effects and effect modification of air pollutants and nSES. Researchers must consider nSES as an effect modifier if not a co-exposure to better understand the complex relationships between air pollution and nSES in urban settings.

Multimodal magnetic resonance imaging reveals distinct sensitivity of hippocampal subfields in asymptomatic stage of Alzheimer’s disease

While hippocampal atrophy and its regional susceptibility to Alzheimer’s disease (AD) are well reported at late stages of AD, studies of the asymptomatic stage of AD are limited but could elucidate early stage pathophysiology as well as provide predictive biomarkers. In this study, we performed multi-modal magnetic resonance imaging (MRI) to estimate morphometry, functional connectivity, and tissue microstructure of hippocampal subfields in cognitively normal adults including those with asymptomatic AD. High-resolution resting-state functional, diffusion and structural MRI, cerebral spinal fluid (CSF), and neuropsychological evaluations were performed in healthy young adults (HY: n = 40) and healthy older adults with negative (HO−: n = 47) and positive (HO+ : n = 25) CSF biomarkers of AD. Morphometry, functional connectivity, and tissue microstructure were estimated from the structural, functional, and diffusion MRI images, respectively. Our results indicated that normal aging affected morphometry, connectivity, and microstructure in all hippocampal subfields, while the subiculum and CA1-3 demonstrated the greatest sensitivity to asymptomatic AD pathology. Tau, rather than amyloid-β, was closely associated with imaging-derived synaptic and microstructural measures. Microstructural metrics were significantly associated with neuropsychological assessments. These findings suggest that the subiculum and CA1-3 are the most vulnerable in asymptomatic AD and tau level is driving these early changes.

Genetic Evidence Supporting a Causal Role of Depression in Alzheimer's Disease

BACKGROUND

Depression has been associated with a higher risk of Alzheimer's disease (AD) in several prospective studies; however, mechanisms underlying this association remain unclear.

METHODS

We examined genetic correlation between depression and AD using linkage disequilibrium score regression. We then tested for evidence of causality between depression and AD using Mendelian randomization and genome-wide association study results. Subsequently, cis and trans quantitative trait locus analyses for the depression genome-wide association study signals were performed to resolve the genetic signals to specific DNA methylation sites, brain transcripts, and proteins. These transcripts and proteins were then examined for associations with AD and its endophenotypes. Finally, the associations between depression polygenic risk score and AD endophenotypes were examined.

RESULTS

We detected a significant genetic correlation between depression and AD, suggesting that they have a shared genetic basis. Furthermore, we found that depression had a causal role in AD through Mendelian randomization but did not find evidence for a causal role of AD on depression. Moreover, we identified 75 brain transcripts and 28 brain proteins regulated by the depression genome-wide association study signals through quantitative trait locus analyses. Of these, 46 transcripts and seven proteins were associated with rates of cognitive decline over time, AD pathologies, and AD diagnosis in two separate cohorts, thus implicating them in AD. In addition, we found that a higher depression polygenic risk score was associated with a faster decline of episodic memory over time.

CONCLUSIONS

Depression appears to have a causal role in AD, and this causal relationship is likely driven, in part, by the 53 brain transcripts and proteins identified in this study.

Alzheimer's disease genetic burden is associated with mid-life depression among persons with normal cognition

INTRODUCTION

Despite an established link between depression and higher Alzheimer's disease (AD) risk, it is unclear whether the conditions share pathophysiology. Here, we investigated whether depression manifesting after age 50 is associated with a genetic predisposition to AD.

METHODS

From the population-based Health and Retirement Study cohort with biennial assessments of depressive symptoms and cognitive performance, we studied 6656 individuals of European ancestry with whole-genome genotyping. Polygenic risk scores (PRSs) for AD were estimated and examined for an association with depression in cognitively normal participants using regression modeling.

RESULTS

Among cognitively normal participants, those with a higher AD PRS were more likely to experience depression after age 50 after accounting for the effects of genetic predisposition to depression, sex, age, and education.

DISCUSSION

Genetic predisposition to AD may be one of the factors contributing to the pathogenesis of mid-life depression. Whether there is a shared genetic basis between mid-life depression and AD merits further study.

The Georgia Memory Net: Implementation of a statewide program to diagnose and treat Alzheimer's disease and related dementias

Objectives

The number of people living with dementia is growing and most patients go years without receiving a specific diagnosis or support services, leading to suboptimal care, negative impacts on the quality of life, and increased costs of care. To address these gaps, the State of Georgia Department of Human Services collaborated with academic and community partners to create the Georgia Memory Net (GMN).

Design

GMN is a hub and spoke model partnered with Emory University's Cognitive Neurology Clinic and Emory Goizueta Alzheimer's Disease Research Center to provide training and support for best practices in diagnosis and management to Memory Assessment Clinics (MACs) throughout the state.

Setting

Communities across the State of Georgia.

Participants

GMN is a mix of academic and community providers, hospital systems, state and community agencies. Patients and families are evaluated at the MACs and connected to community services.

Intervention

A dedicated clinic workflow: primary care providers (PCPs) identify a memory problem and refer to the MACs for diagnostic evaluation; meeting with a community services educator, and development of a care plan. The patient is reconnected with the PCP for continuity of care.

Measurements

Initial metrics include numbers of unique patients, total patient visits, and referrals to state agency partners for community services.

Results

GMN established five MACs across Georgia with annual state funding. Partners at Emory University provided initial training; refined patient workflows for best practices; and provide ongoing support, guidance, and continuing education for MAC teams. Local PCPs and community services partners demonstrated strong engagement with the new model.

Conclusions

GMN is an innovative care model to improve access to accurate and timely diagnosis in patients with memory loss. GMN may help improve the quality of life for patients and families through preventive and early care.

Brain microRNAs are associated with variation in cognitive trajectory in advanced age

In advancing age, some individuals maintain a stable cognitive performance over time, while others experience a rapid decline. Such variation in cognitive trajectory is only partially explained by common neurodegenerative pathologies. Hence, we aimed to identify new molecular processes underlying variation in cognitive trajectory using brain microRNA profile followed by an integrative analysis with brain transcriptome and proteome. Individual cognitive trajectories were derived from longitudinally assessed cognitive-test scores of older-adult brain donors from four longitudinal cohorts. Postmortem brain microRNA profiles, transcriptomes, and proteomes were derived from the dorsolateral prefrontal cortex. The global microRNA association study of cognitive trajectory was performed in a discovery (n = 454) and replication cohort (n = 134), followed by a meta-analysis that identified 6 microRNAs. Among these, miR-132-3p and miR-29a-3p were most significantly associated with cognitive trajectory. They explain 18.2% and 2.0% of the variance of cognitive trajectory, respectively, and act independently of the eight measured neurodegenerative pathologies. Furthermore, integrative transcriptomic and proteomic analyses revealed that miR-132-3p was significantly associated with 24 of the 47 modules of co-expressed genes of the transcriptome, miR-29a-3p with 3 modules, and identified 84 and 214 downstream targets of miR-132-3p and miR-29a-3p, respectively, in cognitive trajectory. This is the first global microRNA study of cognitive trajectory to our knowledge. We identified miR-29a-3p and miR-132-3p as novel and robust contributors to cognitive trajectory independently of the eight known cerebral pathologies. Our findings lay a foundation for future studies investigating mechanisms and developing interventions to enhance cognitive stability in advanced age.

Large-scale deep multi-layer analysis of Alzheimer's disease brain reveals strong proteomic disease-related changes not observed at the RNA level

The biological processes that are disrupted in the Alzheimer's disease (AD) brain remain incompletely understood. In this study, we analyzed the proteomes of more than 1,000 brain tissues to reveal new AD-related protein co-expression modules that were highly preserved across cohorts and brain regions. Nearly half of the protein co-expression modules, including modules significantly altered in AD, were not observed in RNA networks from the same cohorts and brain regions, highlighting the proteopathic nature of AD. Two such AD-associated modules unique to the proteomic network included a module related to MAPK signaling and metabolism and a module related to the matrisome. The matrisome module was influenced by the APOE ε4 allele but was not related to the rate of cognitive decline after adjustment for neuropathology. By contrast, the MAPK/metabolism module was strongly associated with the rate of cognitive decline. Disease-associated modules unique to the proteome are sources of promising therapeutic targets and biomarkers for AD.

Automated analysis of facial emotions in subjects with cognitive impairment

Differences in expressing facial emotions are broadly observed in people with cognitive impairment. However, these differences have been difficult to objectively quantify and systematically evaluate among people with cognitive impairment across disease etiologies and severity. Therefore, a computer vision-based deep learning model for facial emotion recognition trained on 400.000 faces was utilized to analyze facial emotions expressed during a passive viewing memory test. In addition, this study was conducted on a large number of individuals (n = 493), including healthy controls and individuals with cognitive impairment due to diverse underlying etiologies and across different disease stages. Diagnoses included subjective cognitive impairment, Mild Cognitive Impairment (MCI) due to AD, MCI due to other etiologies, dementia due to Alzheimer's diseases (AD), and dementia due to other etiologies (e.g., Vascular Dementia, Frontotemporal Dementia, Lewy Body Dementia, etc.). The Montreal Cognitive Assessment (MoCA) was used to evaluate cognitive performance across all participants. A participant with a score of less than or equal to 24 was considered cognitively impaired (CI). Compared to cognitively unimpaired (CU) participants, CI participants expressed significantly less positive emotions, more negative emotions, and higher facial expressiveness during the test. In addition, classification analysis revealed that facial emotions expressed during the test allowed effective differentiation of CI from CU participants, largely independent of sex, race, age, education level, mood, and eye movements (derived from an eye-tracking-based digital biomarker for cognitive impairment). No screening methods reliably differentiated the underlying etiology of the cognitive impairment. The findings provide quantitative and comprehensive evidence that the expression of facial emotions is significantly different in people with cognitive impairment, and suggests this may be a useful tool for passive screening of cognitive impairment.

Cerebral Aβ deposition in an Aβ-precursor protein-transgenic rhesus monkey

With the ultimate goal of developing a more representative animal model of Alzheimer's disease (AD), two female amyloid-β-(Aβ) precursor protein-transgenic (APPtg) rhesus monkeys were generated by lentiviral transduction of the APP gene into rhesus oocytes, followed by in vitro fertilization and embryo transfer. The APP-transgene included the AD-associated Swedish K670N/M671L and Indiana V717F mutations (APPSWE/IND) regulated by the human polyubiquitin-C promoter. Overexpression of APP was confirmed in lymphocytes and brain tissue. Upon sacrifice at 10 years of age, one of the monkeys had developed Aβ plaques and cerebral Aβ-amyloid angiopathy in the occipital, parietal, and caudal temporal neocortices. The induction of Aβ deposition more than a decade prior to its usual emergence in the rhesus monkey supports the feasibility of creating a transgenic nonhuman primate model for mechanistic analyses and preclinical testing of treatments for Alzheimer's disease and cerebrovascular amyloidosis.

A phase II study repurposing atomoxetine for neuroprotection in mild cognitive impairment

The locus coeruleus (LC) is the initial site of Alzheimer's disease neuropathology, with hyperphosphorylated Tau appearing in early adulthood followed by neurodegeneration in dementia. LC dysfunction contributes to Alzheimer's pathobiology in experimental models, which can be rescued by increasing norepinephrine (NE) transmission. To test NE augmentation as a potential disease-modifying therapy, we performed a biomarker-driven phase II trial of atomoxetine, a clinically-approved NE transporter inhibitor, in subjects with mild cognitive impairment due to Alzheimer's disease. The design was a single-center, 12-month double-blind crossover trial. Thirty-nine participants with mild cognitive impairment (MCI) and biomarker evidence of Alzheimer's disease were randomized to atomoxetine or placebo treatment. Assessments were collected at baseline, 6- (crossover) and 12-months (completer). Target engagement was assessed by CSF and plasma measures of NE and metabolites. Prespecified primary outcomes were CSF levels of IL1α and Thymus-Expressed Chemokine. Secondary/exploratory outcomes included clinical measures, CSF analyses of Aβ42, Tau, and pTau181, mass spectrometry proteomics, and immune-based targeted inflammation-related cytokines, as well as brain imaging with MRI and FDG-PET. Baseline demographic and clinical measures were similar across trial arms. Dropout rates were 5.1% for atomoxetine and 2.7% for placebo, with no significant differences in adverse events. Atomoxetine robustly increased plasma and CSF NE levels. IL-1α and Thymus-Expressed Chemokine were not measurable in most samples. There were no significant treatment effects on cognition and clinical outcomes, as expected given the short trial duration. Atomoxetine was associated with a significant reduction in CSF Tau and pTau181 compared to placebo, but not associated with change in Aβ42. Atomoxetine treatment also significantly altered CSF abundances of protein panels linked to brain pathophysiologies, including synaptic, metabolism, and glial immunity, as well as inflammation-related CDCP1, CD244, TWEAK, and OPG proteins. Treatment was also associated with significantly increased BDNF and reduced triglycerides in plasma. Resting state fMRI showed significantly increased inter-network connectivity due to atomoxetine between the insula and the hippocampus. FDG-PET showed atomoxetine-associated increased uptake in hippocampus, parahippocampal gyrus, middle temporal pole, inferior temporal gyrus, and fusiform gyrus, with carry-over effects six months after treatment. In summary, atomoxetine treatment was safe, well tolerated, and achieved target engagement in prodromal Alzheimer's disease. Atomoxetine significantly reduced CSF Tau and pTau, normalized CSF protein biomarker panels linked to synaptic function, brain metabolism, and glial immunity, and increased brain activity and metabolism in key temporal lobe circuits. Further study of atomoxetine is warranted for repurposing the drug to slow Alzheimer's disease progression.