Pathophysiological subtypes of Alzheimer's disease based on cerebrospinal fluid proteomics

Lysosomal and synaptic dysfunction markers in longitudinal cerebrospinal fluid of de novo Parkinson's disease

Lysosomal and synaptic dysfunctions are hallmarks in neurodegeneration and potentially relevant as biomarkers, but data on early Parkinson's disease (PD) is lacking. We performed targeted mass spectrometry with an established protein panel, assessing autophagy and synaptic function in cerebrospinal fluid (CSF) of drug-naïve de novo PD, and sex-/age-matched healthy controls (HC) cross-sectionally (88 PD, 46 HC) and longitudinally (104 PD, 58 HC) over 10 years. Multiple markers of autophagy, synaptic plasticity, and secretory pathways were reduced in PD. We added samples from prodromal subjects (9 cross-sectional, 12 longitudinal) with isolated REM sleep behavior disorder, revealing secretogranin-2 already decreased compared to controls. Machine learning identified neuronal pentraxin receptor and neurosecretory protein VGF as most relevant for discriminating between groups. CSF levels of LAMP2, neuronal pentraxins, and syntaxins in PD correlated with clinical progression, showing predictive potential for motor- and non-motor symptoms as a valid basis for future drug trials.

Relationship between Plasma Lipid Profile and Cognitive Status in Early Alzheimer Disease

Alzheimer disease (AD) is a heterogeneous and complex disease in which different pathophysiological mechanisms are involved. This heterogenicity can be reflected in different atrophy patterns or clinical manifestations. Regarding biochemical pathways involved in early AD, lipid metabolism plays an important role; therefore, lipid levels have been evaluated as potential AD diagnosis biomarkers, and their levels could be related to different AD clinical manifestations. Therefore, the aim of this work is to study AD lipid profiles from early AD patients and evaluate their clinical significance. For this purpose, untargeted plasma lipidomic analysis was carried out in early AD patients (n = 31) diagnosed with cerebrospinal fluid (CSF) biomarkers. Cluster analysis was carried out to define early AD subgroups according to the lipid levels. Then, the clinical significance of each lipid profile subgroup was studied, analyzing differences for other variables (cognitive status, CSF biomarkers, medication, comorbidities, age, and gender). The cluster analysis revealed two different groups of AD patients. Cluster 1 showed higher levels of plasma lipids and better cognitive status than Cluster 2. However, no differences were found for the other variables (age, gender, medication, comorbidities, cholesterol, and triglycerides levels) between both groups. Plasma lipid levels could differentiate two early AD subgroups, which showed different cognitive statuses. However, further research with a large cohort and longitudinal study evaluating the clinical evolution of these patients is required. In general, it would involve a relevant advance in the knowledge of AD pathological mechanisms, potential treatments, and precision medicine.

Cerebrospinal fluid reference proteins increase accuracy and interpretability of biomarkers for brain diseases

Cerebrospinal fluid (CSF) biomarkers reflect brain pathophysiology and are used extensively in translational research as well as in clinical practice for diagnosis of neurological diseases, e.g., Alzheimer's disease (AD). However, CSF biomarker concentrations may be influenced by non-disease related inter-individual variability. Here we use a data-driven approach to demonstrate the existence of inter-individual variability in mean standardized CSF protein levels. We show that these non-disease related differences cause many commonly reported CSF biomarkers to be highly correlated, thereby producing misleading results if not accounted for. To adjust for this inter-individual variability, we identified and evaluated high-performing reference proteins which improved the diagnostic accuracy of key CSF AD biomarkers. Our reference protein method attenuates the risk for false positive findings, and improves the sensitivity and specificity of CSF biomarkers, with broad implications for both research and clinical practice.

On the effect heterogeneity of established disease susceptibility loci for Alzheimer's disease across different genetic ancestries

INTRODUCTION

Genome-wide association studies have identified numerous disease susceptibility loci (DSLs) for Alzheimer's disease (AD). However, only a limited number of studies have investigated the dependence of the genetic effect size of established DSLs on genetic ancestry.

METHODS

We utilized the whole genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP) including 35,569 participants. A total of 25,459 subjects in four distinct populations (African ancestry, non-Hispanic White, admixed Hispanic, and Asian) were analyzed.

RESULTS

We found that nine DSLs showed significant heterogeneity across populations. Single nucleotide polymorphism (SNP) rs2075650 in translocase of outer mitochondrial membrane 40 (TOMM40) showed the largest heterogeneity (Cochran's Q = 0.00, I2 = 90.08), followed by other SNPs in apolipoprotein C1 (APOC1) and apolipoprotein E (APOE). Two additional loci, signal-induced proliferation-associated 1 like 2 (SIPA1L2) and solute carrier 24 member 4 (SLC24A4), showed significant heterogeneity across populations.

DISCUSSION

We observed substantial heterogeneity for the APOE-harboring 19q13.32 region with TOMM40/APOE/APOC1 genes. The largest risk effect was seen among African Americans, while Asians showed a surprisingly small risk effect.

Large-scale Deep Proteomic Analysis in Alzheimer's Disease Brain Regions Across Race and Ethnicity

Introduction

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease, yet our comprehension predominantly relies on studies within the non-Hispanic White (NHW) population. Here we aimed to provide comprehensive insights into the proteomic landscape of AD across diverse racial and ethnic groups.

Methods

Dorsolateral prefrontal cortex (DLPFC) and superior temporal gyrus (STG) brain tissues were donated from multiple centers (Mayo Clinic, Emory University, Rush University, Mt. Sinai School of Medicine) and were harmonized through neuropathological evaluation, specifically adhering to the Braak staging and CERAD criteria. Among 1105 DLPFC tissue samples (998 unique individuals), 333 were from African American donors, 223 from Latino Americans, 529 from NHW donors, and the rest were from a mixed or unknown racial background. Among 280 STG tissue samples (244 unique individuals), 86 were African American, 76 Latino American, 116 NHW and the rest were mixed or unknown ethnicity. All tissues were uniformly homogenized and analyzed by tandem mass tag mass spectrometry (TMT-MS).

Results

As a Quality control (QC) measure, proteins with more than 50% missing values were removed and iterative principal component analysis was conducted to remove outliers within brain regions. After QC, 9,180 and 9,734 proteins remained in the DLPC and STG proteome, respectively, of which approximately 9,000 proteins were shared between regions. Protein levels of microtubule-associated protein tau (MAPT) and amyloid-precursor protein (APP) demonstrated AD-related elevations in DLPFC tissues with a strong association with CERAD and Braak across racial groups. APOE4 protein levels in brain were highly concordant with APOE genotype of the individuals.

Discussion

This comprehensive region resolved large-scale proteomic dataset provides a resource for the understanding of ethnoracial-specific protein differences in AD brain.

Gold Nanoparticle-Decorated Catalytic Micromotor-Based Aptassay for Rapid Electrochemical Label-Free Amyloid-β42 Oligomer Determination in Clinical Samples from Alzheimer's Patients

Micromotor (MM) technology offers a valuable and smart on-the-move biosensing microscale approach in clinical settings where sample availability is scarce in the case of Alzheimer's disease (AD). Soluble amyloid-β protein oligomers (AβO) (mainly AβO42) that circulate in biological fluids have been recognized as a molecular biomarker and therapeutic target of AD due to their high toxicity, and they are correlated much more strongly with AD compared to the insoluble Aβ monomers. A graphene oxide (GO)-gold nanoparticles (AuNPs)/nickel (Ni)/platinum nanoparticles (PtNPs) micromotors (MMGO-AuNPs)-based electrochemical label-free aptassay is proposed for sensitive, accurate, and rapid determination of AβO42 in complex clinical samples such as brain tissue, cerebrospinal fluid (CSF), and plasma from AD patients. An approach that implies the in situ formation of AuNPs on the GO external layer of tubular MM in only one step during MM electrosynthesis was performed (MMGO-AuNPs). The AβO42 specific thiolated-aptamer (AptAβO42) was immobilized in the MMGO-AuNPs via Au-S interaction, allowing for the selective recognition of the AβO42 (MMGO-AuNPs-AptAβO42-AβO42). AuNPs were smartly used not only to covalently bind a specific thiolated-aptamer for the design of a label-free electrochemical aptassay but also to improve the final MM propulsion performance due to their catalytic activity (approximately 2.0× speed). This on-the-move bioplatform provided a fast (5 min), selective, precise (RSD < 8%), and accurate quantification of AβO42 (recoveries 94-102%) with excellent sensitivity (LOD = 0.10 pg mL-1) and wide linear range (0.5-500 pg mL-1) in ultralow volumes of the clinical sample of AD patients (5 μL), without any dilution. Remarkably, our MM-based bioplatform demonstrated the competitiveness for the determination of AβO42 in the target samples against the dot blot analysis, which requires more than 14 h to provide qualitative results only. It is also important to highlight its applicability to the potential analysis of liquid biopsies as plasma and CSF samples, improving the reliability of the diagnosis given the heterogeneity and temporal complexity of neurodegenerative diseases. The excellent results obtained demonstrate the analytical potency of our approach as a future tool for clinical/POCT (Point-of-care testing) routine scenarios.

Plasma brain-derived tau is an amyloid-associated neurodegeneration biomarker in Alzheimer's disease

Staging amyloid-beta (Aβ) pathophysiology according to the intensity of neurodegeneration could identify individuals at risk for cognitive decline in Alzheimer's disease (AD). In blood, phosphorylated tau (p-tau) associates with Aβ pathophysiology but an AD-type neurodegeneration biomarker has been lacking. In this multicenter study (n = 1076), we show that brain-derived tau (BD-tau) in blood increases according to concomitant Aβ ("A") and neurodegeneration ("N") abnormalities (determined using cerebrospinal fluid biomarkers); We used blood-based A/N biomarkers to profile the participants in this study; individuals with blood-based p-tau+/BD-tau+ profiles had the fastest cognitive decline and atrophy rates, irrespective of the baseline cognitive status. Furthermore, BD-tau showed no or much weaker correlations with age, renal function, other comorbidities/risk factors and self-identified race/ethnicity, compared with other blood biomarkers. Here we show that blood-based BD-tau is a biomarker for identifying Aβ-positive individuals at risk of short-term cognitive decline and atrophy, with implications for clinical trials and implementation of anti-Aβ therapies.

Brain high-throughput multi-omics data reveal molecular heterogeneity in Alzheimer's disease

Unbiased data-driven omic approaches are revealing the molecular heterogeneity of Alzheimer disease. Here, we used machine learning approaches to integrate high-throughput transcriptomic, proteomic, metabolomic, and lipidomic profiles with clinical and neuropathological data from multiple human AD cohorts. We discovered 4 unique multimodal molecular profiles, one of them showing signs of poor cognitive function, a faster pace of disease progression, shorter survival with the disease, severe neurodegeneration and astrogliosis, and reduced levels of metabolomic profiles. We found this molecular profile to be present in multiple affected cortical regions associated with higher Braak tau scores and significant dysregulation of synapse-related genes, endocytosis, phagosome, and mTOR signaling pathways altered in AD early and late stages. AD cross-omics data integration with transcriptomic data from an SNCA mouse model revealed an overlapping signature. Furthermore, we leveraged single-nuclei RNA-seq data to identify distinct cell-types that most likely mediate molecular profiles. Lastly, we identified that the multimodal clusters uncovered cerebrospinal fluid biomarkers poised to monitor AD progression and possibly cognition. Our cross-omics analyses provide novel critical molecular insights into AD.

Biomarker-based staging of Alzheimer disease: rationale and clinical applications

Disease staging, whereby the spatial extent and load of brain pathology are used to estimate the severity of Alzheimer disease (AD), is pivotal to the gold-standard neuropathological diagnosis of AD. Current in vivo diagnostic frameworks for AD are based on abnormal concentrations of amyloid-β and tau in the cerebrospinal fluid or on PET scans, and breakthroughs in molecular imaging have opened up the possibility of in vivo staging of AD. Focusing on the key principles of disease staging shared across several areas of medicine, this Review highlights the potential for in vivo staging of AD to transform our understanding of preclinical AD, refine enrolment criteria for trials of disease-modifying therapies and aid clinical decision-making in the era of anti-amyloid therapeutics. We provide a state-of-the-art review of recent biomarker-based AD staging systems and highlight their contributions to the understanding of the natural history of AD. Furthermore, we outline hypothetical frameworks to stage AD severity using more accessible fluid biomarkers. In addition, by applying amyloid PET-based staging to recently published anti-amyloid therapeutic trials, we highlight how biomarker-based disease staging frameworks could illustrate the numerous pathological changes that have already taken place in individuals with mildly symptomatic AD. Finally, we discuss challenges related to the validation and standardization of disease staging and provide a forward-looking perspective on potential clinical applications.

Multi-cohort cerebrospinal fluid proteomics identifies robust molecular signatures for asymptomatic and symptomatic Alzheimer's disease

Changes in Amyloid-β (A), hyperphosphorylated Tau (T) in brain and cerebrospinal fluid (CSF) precedes AD symptoms, making CSF proteome a potential avenue to understand the pathophysiology and facilitate reliable diagnostics and therapies. Using the AT framework and a three-stage study design (discovery, replication, and meta-analysis), we identified 2,173 proteins dysregulated in AD, that were further validated in a third totally independent cohort. Machine learning was implemented to create and validate highly accurate and replicable (AUC>0.90) models that predict AD biomarker positivity and clinical status. These models can also identify people that will convert to AD and those AD cases with faster progression. The associated proteins cluster in four different protein pseudo-trajectories groups spanning the AD continuum and were enrichment in specific pathways including neuronal death, apoptosis and tau phosphorylation (early stages), microglia dysregulation and endolysosomal dysfuncton(mid-stages), brain plasticity and longevity (mid-stages) and late microglia-neuron crosstalk (late stages).

Data-driven modelling of neurodegenerative disease progression: thinking outside the black box

Data-driven disease progression models are an emerging set of computational tools that reconstruct disease timelines for long-term chronic diseases, providing unique insights into disease processes and their underlying mechanisms. Such methods combine a priori human knowledge and assumptions with large-scale data processing and parameter estimation to infer long-term disease trajectories from short-term data. In contrast to 'black box' machine learning tools, data-driven disease progression models typically require fewer data and are inherently interpretable, thereby aiding disease understanding in addition to enabling classification, prediction and stratification. In this Review, we place the current landscape of data-driven disease progression models in a general framework and discuss their enhanced utility for constructing a disease timeline compared with wider machine learning tools that construct static disease profiles. We review the insights they have enabled across multiple neurodegenerative diseases, notably Alzheimer disease, for applications such as determining temporal trajectories of disease biomarkers, testing hypotheses about disease mechanisms and uncovering disease subtypes. We outline key areas for technological development and translation to a broader range of neuroscience and non-neuroscience applications. Finally, we discuss potential pathways and barriers to integrating disease progression models into clinical practice and trial settings.

Associations of cerebrospinal fluid complement proteins with Alzheimer's pathology, cognition, and brain structure in non-dementia elderly

BACKGROUND

Cerebrospinal fluid (CSF) complement activation is a key part of neuroinflammation that occurs in the early stages of Alzheimer's disease (AD). However, the associations of CSF complement proteins with AD pathology, cognition, and structural neuroimaging biomarkers for AD have been rarely investigated.

METHODS

A total of 210 participants (125 mild cognitive impairment [MCI] patients and 85 normal controls) were included from Alzheimer's Disease Neuroimaging Initiative (ADNI) database who measured AD pathology, cognition, and neuroimaging at baseline and every 12 months. The mixed-effect linear models were utilized to investigate longitudinal associations of CSF complement proteins with AD pathology, cognition, and neuroimaging in cognitively normal (CN) and mild cognitive impairment (MCI) subjects. Causal mediation analyses were conducted to explore the potential mediators between CSF complement proteins and cognitive changes.

RESULTS

We found that the subjects with low CSF complement protein levels at baseline had worse outcomes in AD pathology, indicated by their lowest concentrations observed in A + and A + T + individuals. The reduced CSF complement proteins were associated with faster accumulation of tau among CN subjects and with cognitive decline and greater brain atrophy of specific regions among MCI subjects. Furthermore, mediation analyses showed that the effects of CSF complement proteins on cognitive performance were partially mediated by regional brain structures (mediation proportions range from 19.78 to 94.92%; p < 0.05).

CONCLUSIONS

This study demonstrated that CSF complement proteins were involved in the early progression of AD. Our results indicated that regional brain atrophy might be a plausible way to connect CSF complement protein levels and cognition.

Astragalus polysaccharides promote neural stem cells-derived oligodendrogenesis through attenuating CD8+T cell infiltration in experimental autoimmune encephalomyelitis

Endogenous neural stem cells (NSCs) have the potential to generate remyelinating oligodendrocytes, which play an important role in multiple sclerosis (MS). However, the differentiation of NSCs into oligodendrocytes is insufficient, which is considered a major cause of remyelination failure. Our previous work reported that Astragalus polysaccharides (APS) had a neuroprotective effect on experimental autoimmune encephalomyelitis (EAE) mice. However, it remains unclear whether APS regulate NSCs differentiation in EAE mice. In this study, our data illustrated that APS administration could promote NSCs in the subventricular zone (SVZ) to differentiate into oligodendrocytes. Furthermore, we found that APS significantly improved neuroinflammation and inhibited CD8+T cell infiltration into SVZ of EAE mice. We also found that MOG35-55-specific CD8+T cells suppressed NSCs differentiation into oligodendrocytes by secreting IFN-γ, and APS facilitated the differentiation of NSCs into oligodendrocytes which was related to decreased IFN-γ secretion. In addition, APS treatment did not show a better effect on the NSCs-derived oligodendrogenesis after CD8+T cell depletion. This present study demonstrated that APS alleviated neuroinflammation and CD8+T cell infiltration into SVZ to induce oligodendroglial differentiation, and thus exerted neuroprotective effect. Our findings revealed that reducing the infiltration of CD8+T cells might contribute to enhancing NSCs-derived neurogenesis. And APS might be a promising drug candidate to treat MS.

Proteomics: Unraveling the Cross Talk Between Innate Immunity and Disease Pathophysiology, Diagnostics, and Treatment Options

Inflammation is crucial in diseases, and proteins play a key role in the interplay between innate immunity and pathology. This review explores how proteomics helps understanding this relationship, focusing on diagnosis and treatment. We explore the dynamic innate response and the significance of proteomic techniques in deciphering the complex network of proteins involved in prevalent diseases, including infections, cancer, autoimmune and neurodegenerative disorders. Proteomics identifies key proteins in host-pathogen interactions, shedding light on infection mechanisms and inflammation. These discoveries hold promise for diagnostic tools, therapies, and vaccines. In cancer research, proteomics reveals innate signatures associated with tumor development, immune evasion, and therapeutic response. Additionally, proteomic analysis has unveiled autoantigens and dysregulation of the innate immune system in autoimmunity, offering opportunities for early diagnosis, disease monitoring, and new therapeutic targets. Moreover, proteomic analysis has identified altered protein expression patterns in neurodegenerative diseases like Alzheimer's and Parkinson's, providing insights into potential therapeutic strategies. Proteomics of the innate immune system provides a comprehensive understanding of disease mechanisms, identifies biomarkers, and enables effective interventions in various diseases. Despite still in its early stages, this approach holds great promise to revolutionize innate immunity research and significantly improve patient outcomes across a wide range of diseases.

Effect of Vascular Risk Factors on Blood-Brain Barrier and Cerebrospinal Fluid Biomarkers Along the Alzheimer's Disease Continuum: A Retrospective Observational Study

BACKGROUND

Blood-brain barrier (BBB) dysfunction could favor the pathogenesis and progression of Alzheimer's disease (AD). Vascular risk factors (VRF) could worsen BBB integrity, thus promoting neurode generation.

OBJECTIVE

To investigate BBB permeability and its relation with VRF along the AD continuum (ADc). Cerebrospinal fluid (CSF) Amyloid (A) and p-tau (T) levels were used to stratify patients.

METHODS

We compared CSF/plasma albumin ratio (QAlb) of 131 AD patients and 24 healthy controls (HC). APOE genotype and VRF were evaluated for each patient. Spearman's Rho correlation was used to investigate the associations between Qalb and CSF AD biomarkers. Multivariate regression analyses were conducted to explore the relationship between Qalb and AD biomarkers, sex, age, cognitive status, and VRF.

RESULTS

QAlb levels did not show significant difference between ADc patients and HC (p = 0.984). However, QAlb was significantly higher in A + T-compared to A + T+ (p = 0.021). In ADc, CSF p-tau demonstrated an inverse correlation with QAlb, a finding confirmed in APOE4 carriers (p = 0.002), but not in APOE3. Furthermore, in APOE4 carriers, sex, hypertension, and hypercholesterolemia were associated with QAlb (p = 0.004, p = 0.038, p = 0.038, respectively), whereas only sex showed an association in APOE3 carriers (p = 0.026).

CONCLUSIONS

BBB integrity is preserved in ADc. Among AT categories, A + T-have a more permeable BBB than A + T+. In APOE4 carriers, CSF p-tau levels display an inverse association with BBB permeability, which in turn, seems to be affected by VRF. These data suggest a possible relationship between BBB efficiency, VRF and CSF p-tau levels depending on APOE genotype.

The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022

The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co-pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI-4 aims to address by enrolling a diverse cohort.

Blood-brain barrier permeability is associated with different neuroinflammatory profiles in Alzheimer's disease

INTRODUCTION

Inflammation is an important player in Alzheimer's disease (AD), whose effects can be influenced by the blood-brain barrier (BBB). Here, we investigated the relationship between BBB permeability, indicated by cerebrospinal fluid (CSF)/plasma albumin quotient (Qalb), and CSF indexes of neuroinflammation in a cohort of biologically defined AD patients.

METHODS

Fifty-nine consecutive patients with mild cognitive impairment (MCI) or early AD (Mini-Mental State Examination [MMSE] >22) underwent CSF analysis for inflammatory cytokines (interleukin [IL]-1β, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, Il-10, IL-12, IL-13, IL-17, tumor necrosis factor-α [TNF-α], interferon-γ [IFN-γ], granulocyte-monocyte colony-stimulating factor [GM-CSF], granulocyte colony-stimulating factor [G-CSF]). Using backward stepwise linear regression analysis, we explored the potential influence of each cytokine CSF level on Qalb considering age, sex, and apolipoprotein E (APOE) as covariates.

RESULTS

Higher levels of IL-4 (β = 0.356, 0.005) and IL-8 (β = 0.249, 0.05) were associated with higher Qalb values, while macrophage inflammatory protein-1α (MIP-1β) (β = -0.274; p = 0.032) and TNF-α (β = -0.248; p = 0.031) showed a significant negative association with BBB permeability. Age was also positively associated with Qalb (β = 0.283; p = 0.016).

CONCLUSIONS

Despite the overall integrity of the BBB, its permeability could either influence or be influenced by central neuroinflammation, reflected by CSF cytokine levels. This is in line with previous studies that showed that patients with a more intact barrier are those with more prominent neurodegeneration. Our findings suggest that different neuroinflammatory profiles can be associated with different levels of BBB permeability in AD.

Cerebrospinal fluid proteomics in patients with Alzheimer's disease reveals five molecular subtypes with distinct genetic risk profiles

Alzheimer's disease (AD) is heterogenous at the molecular level. Understanding this heterogeneity is critical for AD drug development. Here we define AD molecular subtypes using mass spectrometry proteomics in cerebrospinal fluid, based on 1,058 proteins, with different levels in individuals with AD (n = 419) compared to controls (n = 187). These AD subtypes had alterations in protein levels that were associated with distinct molecular processes: subtype 1 was characterized by proteins related to neuronal hyperplasticity; subtype 2 by innate immune activation; subtype 3 by RNA dysregulation; subtype 4 by choroid plexus dysfunction; and subtype 5 by blood-brain barrier impairment. Each subtype was related to specific AD genetic risk variants, for example, subtype 1 was enriched with TREM2 R47H. Subtypes also differed in clinical outcomes, survival times and anatomical patterns of brain atrophy. These results indicate molecular heterogeneity in AD and highlight the need for personalized medicine.

Artificial intelligence for dementia drug discovery and trials optimization

Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi-disciplinary approach can promote data-driven decision-making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation.

Shifting the trajectory of therapeutic development for neurological and psychiatric disorders

Clinical trials for central nervous system disorders often enroll patients with unrecognized heterogeneous diseases, leading to costly trials that have high failure rates. Here, we discuss the potential of emerging technologies and datasets to elucidate disease mechanisms and identify biomarkers to improve patient stratification and monitoring of disease progression in clinical trials for neuropsychiatric disorders. Greater efforts must be centered on rigorously standardizing data collection and sharing of methods, datasets, and analytical tools across sectors. To address health care disparities in clinical trials, diversity of genetic ancestries and environmental exposures of research participants and associated biological samples must be prioritized.

Alzheimer's disease heterogeneity explained by polygenic risk scores derived from brain transcriptomic profiles

INTRODUCTION

Alzheimer's disease (AD) is heterogeneous, both clinically and neuropathologically. We investigated whether polygenic risk scores (PRSs) integrated with transcriptome profiles from AD brains can explain AD clinical heterogeneity.

METHODS

We conducted co-expression network analysis and identified gene sets (modules) that were preserved in three AD transcriptome datasets and associated with AD-related neuropathological traits including neuritic plaques (NPs) and neurofibrillary tangles (NFTs). We computed the module-based PRSs (mbPRSs) for each module and tested associations with mbPRSs for cognitive test scores, cognitively defined AD subgroups, and brain imaging data.

RESULTS

Of the modules significantly associated with NPs and/or NFTs, the mbPRSs from two modules (M6 and M9) showed distinct associations with language and visuospatial functioning, respectively. They matched clinical subtypes and brain atrophy at specific regions.

DISCUSSION

Our findings demonstrate that polygenic profiling based on co-expressed gene sets can explain heterogeneity in AD patients, enabling genetically informed patient stratification and precision medicine in AD.

HIGHLIGHTS

Co-expression gene-network analysis in Alzheimer's disease (AD) brains identified gene sets (modules) associated with AD heterogeneity. AD-associated modules were selected when genes in each module were enriched for neuritic plaques and neurofibrillary tangles. Polygenic risk scores from two selected modules were linked to the matching cognitively defined AD subgroups (language and visuospatial subgroups). Polygenic risk scores from the two modules were associated with cognitive performance in language and visuospatial domains and the associations were confirmed in regional-specific brain atrophy data.

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.

Heterogeneity of Alzheimer's disease identified by neuropsychological test profiling

Alzheimer's disease (AD) is a highly heterogeneous disorder. Untangling this variability could lead to personalized treatments and improve participant recruitment for clinical trials. We investigated the cognitive subgroups by using a data-driven clustering technique in an AD cohort. People with mild-moderate probable AD from Taiwan was included. Neuropsychological test results from the Cognitive Abilities Screening Instrument were clustered using nonnegative matrix factorization. We identified two clusters in 112 patients with predominant deficits in memory (62.5%) and non-memory (37.5%) cognitive domains, respectively. The memory group performed worse in short-term memory and orientation and better in attention than the non-memory group. At baseline, patients in the memory group had worse global cognitive status and dementia severity. Linear mixed effect model did not reveal difference in disease trajectory within 3 years of follow-up between the two clusters. Our results provide insights into the cognitive heterogeneity in probable AD in an Asian population.

Sulfur-bridging the gap: investigating the electrochemistry of novel copper chelating agents for Alzheimer's disease applications

There is currently an unmet demand for multi-functional precision treatments for Alzheimer's disease (AD) after several failed attempts at designing drugs based on the amyloid hypothesis. The focus of this work is to investigate sulfur-bridged quinoline ligands that could potentially be used in chelation therapies for a subpopulation of AD patients presenting with an overload of labile copper ions, which are known to catalyze the production of reactive oxygen species (ROS) and exacerbate other markers of AD progression. The ligands 1-(2'-thiopyridyl)isoquinoline (1TPIQ) and 2-(2'-thiopyridyl)quinoline (2TPQ) were synthesized and characterized before being electrochemically investigated in the presence of different oxidizing and reducing agents in solution with a physiological pH relevant to the brain. The electrochemical response of each compound with copper was studied by employing both hydrogen peroxide (H2O2) as an oxidizing agent and ascorbic acid (AA) as an antioxidant during analysis using cyclic voltammetry (CV). The cyclic voltammograms of each quinoline were compared with similar ligands that contained aromatic N-donor groups but no sulfur groups to provide relative electrochemical properties of each complex in solution. In a dose-dependent manner, it was observed that AA exerted dual-efficacy when combined with these chelating ligands: promoting synergistic metal binding while also scavenging harmful ROS, suggesting AA is an effective adjuvant therapeutic agent. Overall, this study shows how coordination by sulfur-bridged quinoline ligands can alter copper electrochemistry in the presence of AA to limit ROS production in solution.

SCRN1: A cerebrospinal fluid biomarker correlating with tau in Alzheimer's disease

INTRODUCTION

Secernin-1 (SCRN1) is a neuronal protein that co-localizes with neurofibrillary tangles in Alzheimer's disease (AD), but not with tau inclusions in corticobasal degeneration (CBD), progressive supranuclear palsy (PSP), or Pick's disease.

METHODS

We measured SCRN1 concentration in cerebrospinal fluid (CSF) using a novel mass spectrometric parallel reaction monitoring method in three clinical cohorts comprising patients with neurochemically characterized AD (n = 25) and controls (n = 28), clinically diagnosed Parkinson's disease (PD; n = 38), multiple system atrophy (MSA; n = 31), PSP (n = 20), CBD (n = 8), healthy controls (n = 37), and neuropathology-confirmed AD (n = 47).

RESULTS

CSF SCRN1 was significantly increased in AD (P < 0.01, fold change = 1.4) compared to controls (receiver operating characteristic area under the curve = 0.78) but not in CBD, PSP, PD, or MSA. CSF SCRN1 positively correlated with CSF total tau (R = 0.78, P = 1.1 × 10-13 ), phosphorylated tau181 (R = 0.64, P = 3.2 × 10-8 ), and Braak stage and negatively correlated with Mini-Mental State Examination score.

DISCUSSION

CSF SCRN1 is a candidate biomarker of AD, reflecting tau pathology.

HIGHLIGHTS

We developed a parallel reaction monitoring assay to measure secernin-1 (SCRN1) in cerebrospinal fluid (CSF). CSF SCRN1 was increased in Alzheimer's disease compared to healthy controls. CSF SCRN1 remained unchanged in Parkinson's disease, multiple system atrophy, progressive supranuclear palsy, or corticobasal degeneration compared to controls. CSF SCRN1 correlated strongly with CSF phosphorylated tau and total tau. CSF SCRN1 increased across Braak stages and negatively correlated with Mini-Mental State Examination score.

Temporal pathway analysis of cerebrospinal fluid proteome in herpes simplex encephalitis

OBJECTIVES

We examined the temporal changes of the CSF proteome in patients with herpes simplex encephalitis (HSE) during the course of the disease, in relation to anti-N-methyl-D-aspartate receptor (NMDAR) serostatus, corticosteroid treatment, brain MRI and neurocognitive performance.

METHODS

Patients were retrospectively included from a previous prospective trial with a pre-specified CSF sampling protocol. Mass spectrometry data of the CSF proteome were processed using pathway analysis.

RESULTS

We included 48 patients (110 CSF samples). Samples were grouped based on time of collection relative to hospital admission - T1: ≤ 9 d, T2: 13-28 d, T3: ≥ 68 d. At T1, a strong multi-pathway response was seen including acute phase response, antimicrobial pattern recognition, glycolysis and gluconeogenesis. At T2, most pathways activated at T1 were no longer significantly different from T3. After correction for multiplicity and considering the effect size threshold, 6 proteins were significantly less abundant in anti-NMDAR seropositive patients compared to seronegative: procathepsin H, heparin cofactor 2, complement factor I, protein AMBP, apolipoprotein A1 and polymeric immunoglobulin receptor. No significant differences in individual protein levels were found in relation to corticosteroid treatment, size of brain MRI lesion or neurocognitive performance.

CONCLUSIONS

We show a temporal change in the CSF proteome in HSE patients during the course of the disease. This study provides insight into quantitative and qualitative aspects of the dynamic pathophysiology and pathway activation patterns in HSE and prompts for future studies on the role of apolipoprotein A1 in HSE, which has previously been associated with NMDAR encephalitis.

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.

A microglial activity state biomarker panel differentiates FTD-granulin and Alzheimer's disease patients from controls

BACKGROUND

With the emergence of microglia-modulating therapies there is an urgent need for reliable biomarkers to evaluate microglial activation states.

METHODS

Using mouse models and human induced pluripotent stem cell-derived microglia (hiMGL), genetically modified to yield the most opposite homeostatic (TREM2-knockout) and disease-associated (GRN-knockout) states, we identified microglia activity-dependent markers. Non-targeted mass spectrometry was used to identify proteomic changes in microglia and cerebrospinal fluid (CSF) of Grn- and Trem2-knockout mice. Additionally, we analyzed the proteome of GRN- and TREM2-knockout hiMGL and their conditioned media. Candidate marker proteins were tested in two independent patient cohorts, the ALLFTD cohort (GRN mutation carriers versus non-carriers), as well as the proteomic data set available from the EMIF-AD MBD study.

RESULTS

We identified proteomic changes between the opposite activation states in mouse microglia and CSF, as well as in hiMGL cell lysates and conditioned media. For further verification, we analyzed the CSF proteome of heterozygous GRN mutation carriers suffering from frontotemporal dementia (FTD). We identified a panel of six proteins (FABP3, MDH1, GDI1, CAPG, CD44, GPNMB) as potential indicators for microglial activation. Moreover, we confirmed three of these proteins (FABP3, GDI1, MDH1) to be significantly elevated in the CSF of Alzheimer's (AD) patients. Remarkably, each of these markers differentiated amyloid-positive cases with mild cognitive impairment (MCI) from amyloid-negative individuals.

CONCLUSIONS

The identified candidate proteins reflect microglia activity and may be relevant for monitoring the microglial response in clinical practice and clinical trials modulating microglial activity and amyloid deposition. Moreover, the finding that three of these markers differentiate amyloid-positive from amyloid-negative MCI cases in the AD cohort suggests that these proteins associate with a very early immune response to seeded amyloid. This is consistent with our previous findings in the Dominantly Inherited Alzheimer's Disease Network (DIAN) cohort, where soluble TREM2 increases as early as 21 years before symptom onset. Moreover, in mouse models for amyloidogenesis, seeding of amyloid is limited by physiologically active microglia further supporting their early protective role. The biological functions of some of our main candidates (FABP3, CD44, GPNMB) also further emphasize that lipid dysmetabolism may be a common feature of neurodegenerative disorders.

Efficacy and safety of anti-amyloid-β monoclonal antibodies in current Alzheimer's disease phase III clinical trials: A systematic review and interactive web app-based meta-analysis

The risk-benefit profile of anti-Aβ monoclonal antibodies (mAbs) in Alzheimer's disease (AD) remains unclear, especially concerning their safety and overall effects on AD progression and cognitive function. Here, we investigated cognitive, biomarker and side effects of anti-Aβ mAbs in large phase III randomized placebo-controlled clinical trials (RCTs) in sporadic AD. The search was performed on Google Scholar, PubMed and ClinicalTrials.gov by applying Jadad score to evaluate the methodological quality of the reports. Studies were excluded if they scored < 3 on Jadad scale or if they analyzed less than 200 sporadic AD patients. We followed PRISMA guidelines and DerSimonian-Laird random-effects model in R. Primary outcomes were cognitive: AD Assessment Scale-Cognitive Subscale (ADAS-Cog), Mini Mental State Examination (MMSE) and Clinical Dementia Rating Scale-sum of Boxes (CDR-SB). Secondary and tertiary outcomes included biomarkers of Aβ and tau pathology, adverse events, and performance on Alzheimer's Disease Cooperative Study - Activities of Daily Living Scale. The meta-analysis included 14,980 patients in 14 studies and four mAbs: Bapineuzumab, Aducanumab, Solanezumab and Lecanemab. The results of this study suggest that anti-Aβ mAbs statistically improved cognitive and biomarker outcomes, particularly Aducanumab and Lecanemab. However, while cognitive effects were of small effect sizes, these drugs considerably increased risk of side effects such as Amyloid Related Imaging Abnormalities (ARIA), especially in APOE-ε4 carriers. Meta-regression revealed that higher (better) baseline MMSE score was associated with improved ADAS Cog and CDR-SB. In order to improve reproducibility and update the analysis in the future, we developed AlzMeta.app, web-based application freely available at https://alzmetaapp.shinyapps.io/alzmeta/.

Different associations between amyloid-βeta 42, amyloid-βeta 40, and amyloid-βeta 42/40 with soluble phosphorylated-tau and disease burden in Alzheimer's disease: a cerebrospinal fluid and fluorodeoxyglucose-positron emission tomography study

BACKGROUND

Despite the high sensitivity of cerebrospinal fluid (CSF) amyloid beta (Aβ)42 to detect amyloid pathology, the Aβ42/Aβ40 ratio (amyR) better estimates amyloid load, with higher specificity for Alzheimer's disease (AD). However, whether Aβ42 and amyR have different meanings and whether Aβ40 represents more than an Aβ42-corrective factor remain to be clarified. Our study aimed to compare the ability of Aβ42 and amyR to detect AD pathology in terms of p-tau/Aβ42 ratio and brain glucose metabolic patterns using fluorodeoxyglucose-positron emission tomography (FDG-PET).

METHODS

CSF biomarkers were analyzed with EUROIMMUN ELISA. We included 163 patients showing pathological CSF Aβ42 and normal p-tau (A + T -  = 98) or pathological p-tau levels (A + T +  = 65) and 36 control subjects (A - T -). A + T - patients were further stratified into those with normal (CSFAβ42 + /amyR -  = 46) and pathological amyR (CSFAβ42 + /amyR +  = 52). We used two distinct cut-offs to determine pathological values of p-tau/Aβ42: (1) ≥ 0.086 and (2) ≥ 0.122. FDG-PET patterns were evaluated in a subsample of patients (n = 46) and compared to 24 controls.

RESULTS

CSF Aβ40 levels were the lowest in A - T - and in CSFAβ42 + /amyR - , higher in CSFAβ42 + /amyR + and highest in A + T + (F = 50.75; p < 0.001), resembling CSF levels of p-tau (F = 192; p < 0.001). We found a positive association between Aβ40 and p-tau in A - T - (β = 0.58; p < 0.001), CSFAβ42 + /amyR - (β = 0.47; p < 0.001), and CSFAβ42 + /amyR + patients (β = 0.48; p < 0.001) but not in A + T + . Investigating biomarker changes as a function of amyR, we observed a weak variation in CSF p-tau (+ 2 z-scores) and Aβ40 (+ 0.8 z-scores) in the normal amyR range, becoming steeper over the pathological threshold of amyR (p-tau: + 5 z-scores, Aβ40: + 4.5 z-score). CSFAβ42 + /amyR + patients showed a significantly higher probability of having pathological p-tau/Aβ42 than CSFAβ42 + /amyR - (cut-off ≥ 0.086: OR 23.3; cut-off ≥ 0.122: OR 8.8), which however still showed pathological values of p-tau/Aβ42 in some cases (cut-off ≥ 0.086: 35.7%; cut-off ≥ 0.122: 17.3%) unlike A - T - . Accordingly, we found reduced FDG metabolism in the temporoparietal regions of CSFAβ42 + /amyR - compared to controls, and further reduction in frontal areas in CSFAβ42 + /amyR + , like in A + T + .

CONCLUSIONS

Pathological p-tau/Aβ42 and FDG hypometabolism typical of AD can be found in patients with decreased CSF Aβ42 levels alone. AmyR positivity, associated with higher Aβ40 levels, is accompanied by higher CSF p-tau and widespread FDG hypometabolism.

Alzheimer's disease biomarker profiling in a memory clinic cohort without common comorbidities

Alzheimer's disease is a multifactorial disorder with large heterogeneity. Comorbidities such as hypertension, hypercholesterolaemia and diabetes are known contributors to disease progression. However, less is known about their mechanistic contribution to Alzheimer's pathology and neurodegeneration. The aim of this study was to investigate the relationship of several biomarkers related to risk mechanisms in Alzheimer's disease with the well-established Alzheimer's disease markers in a memory clinic population without common comorbidities. We investigated 13 molecular markers representing key mechanisms underlying Alzheimer's disease pathogenesis in CSF from memory clinic patients without diagnosed hypertension, hypercholesterolaemia or diabetes nor other neurodegenerative disorders. An analysis of covariance was used to compare biomarker levels between clinical groups. Associations were analysed by linear regression. Two-step cluster analysis was used to determine patient clusters. Two key markers were analysed by immunofluorescence staining in the hippocampus of non-demented control and Alzheimer's disease individuals. CSF samples from a total of 90 participants were included in this study: 30 from patients with subjective cognitive decline (age 62.4 ± 4.38, female 60%), 30 with mild cognitive impairment (age 65.6 ± 7.48, female 50%) and 30 with Alzheimer's disease (age 68.2 ± 7.86, female 50%). Angiotensinogen, thioredoxin-1 and interleukin-15 had the most prominent associations with Alzheimer's disease pathology, synaptic and axonal damage markers. Synaptosomal-associated protein 25 kDa and neurofilament light chain were increased in mild cognitive impairment and Alzheimer's disease patients. Grouping biomarkers by biological function showed that inflammatory and survival components were associated with Alzheimer's disease pathology, synaptic dysfunction and axonal damage. Moreover, a vascular/metabolic component was associated with synaptic dysfunction. In the data-driven analysis, two patient clusters were identified: Cluster 1 had increased CSF markers of oxidative stress, vascular pathology and neuroinflammation and was characterized by elevated synaptic and axonal damage, compared with Cluster 2. Clinical groups were evenly distributed between the clusters. An analysis of post-mortem hippocampal tissue showed that compared with non-demented controls, angiotensinogen staining was higher in Alzheimer's disease and co-localized with phosphorylated-tau. The identification of biomarker-driven endophenotypes in cognitive disorder patients further highlights the biological heterogeneity of Alzheimer's disease and the importance of tailored prevention and treatment strategies.

Protein disulfide isomerases as CSF biomarkers for the neuronal response to tau pathology

INTRODUCTION

Cerebrospinal fluid (CSF) biomarkers for specific cellular disease processes are lacking for tauopathies. In this translational study we aimed to identify CSF biomarkers reflecting early tau pathology-associated unfolded protein response (UPR) activation.

METHODS

We employed mass spectrometry proteomics and targeted immunoanalysis in a combination of biomarker discovery in primary mouse neurons in vitro and validation in patient CSF from two independent large multicentre cohorts (EMIF-AD MBD, n = 310; PRIDE, n = 771).

RESULTS

First, we identify members of the protein disulfide isomerase (PDI) family in the neuronal UPR-activated secretome and validate secretion upon tau aggregation in vitro. Next, we demonstrate that PDIA1 and PDIA3 levels correlate with total- and phosphorylated-tau levels in CSF. PDIA1 levels are increased in CSF from AD patients compared to controls and patients with tau-unrelated frontotemporal and Lewy body dementia (LBD).

HIGHLIGHTS

Neuronal unfolded protein response (UPR) activation induces the secretion of protein disulfide isomerases (PDIs) in vitro. PDIA1 is secreted upon tau aggregation in neurons in vitro. PDIA1 and PDIA3 levels correlate with total and phosphorylated tau levels in CSF. PDIA1 levels are increased in CSF from Alzheimer's disease (AD) patients compared to controls. PDIA1 levels are not increased in CSF from tau-unrelated frontotemporal dementia (FTD) and Lewy body dementia (LBD) patients.

Multiomics profiling of human plasma and cerebrospinal fluid reveals ATN-derived networks and highlights causal links in Alzheimer's disease

INTRODUCTION

This study employed an integrative system and causal inference approach to explore molecular signatures in blood and CSF, the amyloid/tau/neurodegeneration [AT(N)] framework, mild cognitive impairment (MCI) conversion to Alzheimer's disease (AD), and genetic risk for AD.

METHODS

Using the European Medical Information Framework (EMIF)-AD cohort, we measured 696 proteins in cerebrospinal fluid (n = 371), 4001 proteins in plasma (n = 972), 611 metabolites in plasma (n = 696), and genotyped whole-blood (7,778,465 autosomal single nucleotide epolymorphisms, n = 936). We investigated associations: molecular modules to AT(N), module hubs with AD Polygenic Risk scores and APOE4 genotypes, molecular hubs to MCI conversion and probed for causality with AD using Mendelian randomization (MR).

RESULTS

AT(N) framework associated with protein and lipid hubs. In plasma, Proprotein Convertase Subtilisin/Kexin Type 7 showed evidence for causal associations with AD. AD was causally associated with Reticulocalbin 2 and sphingomyelins, an association driven by the APOE isoform.

DISCUSSION

This study reveals multi-omics networks associated with AT(N) and causal AD molecular candidates.

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.

Proteomic clusters underlie heterogeneity in preclinical Alzheimer's disease progression

Heterogeneity in progression to Alzheimer's disease (AD) poses challenges for both clinical prognosis and clinical trial implementation. Multiple AD-related subtypes have previously been identified, suggesting differences in receptivity to drug interventions. We identified early differences in preclinical AD biomarkers, assessed patterns for developing preclinical AD across the amyloid-tau-(neurodegeneration) [AT(N)] framework, and considered potential sources of difference by analysing the CSF proteome. Participants (n = 10) enrolled in longitudinal studies at the Knight Alzheimer Disease Research Center completed four or more lumbar punctures. These individuals were cognitively normal at baseline. Cerebrospinal fluid measures of amyloid-β (Aβ)42, phosphorylated tau (pTau181), and neurofilament light chain (NfL) as well as proteomics values were evaluated. Imaging biomarkers, including PET amyloid and tau, and structural MRI, were repeatedly obtained when available. Individuals were staged according to the amyloid-tau-(neurodegeneration) framework. Growth mixture modelling, an unsupervised clustering technique, identified three patterns of biomarker progression as measured by CSF pTau181 and Aβ42. Two groups (AD Biomarker Positive and Intermediate AD Biomarker) showed distinct progression from normal biomarker status to having biomarkers consistent with preclinical AD. A third group (AD Biomarker Negative) did not develop abnormal AD biomarkers over time. Participants grouped by CSF trajectories were re-classified using only proteomic profiles (AUCAD Biomarker Positive versus AD Biomarker Negative = 0.857, AUCAD Biomarker Positive versus Intermediate AD Biomarkers = 0.525, AUCIntermediate AD Biomarkers versus AD Biomarker Negative = 0.952). We highlight heterogeneity in the development of AD biomarkers in cognitively normal individuals. We identified some individuals who became amyloid positive before the age of 50 years. A second group, Intermediate AD Biomarkers, developed elevated CSF ptau181 significantly before becoming amyloid positive. A third group were AD Biomarker Negative over repeated testing. Our results could influence the selection of participants for specific treatments (e.g. amyloid-reducing versus other agents) in clinical trials. CSF proteome analysis highlighted additional non-AT(N) biomarkers for potential therapies, including blood-brain barrier-, vascular-, immune-, and neuroinflammatory-related targets.

Next-generation proteomics technologies in Alzheimer's disease: from clinical research to routine diagnostics

INTRODUCTION

Clinical proteomics studies of Alzheimer's disease (AD) research aim to identify biomarkers useful for clinical research, diagnostics, and improve our understanding of the pathological processes involved in the disease. The rapidly increasing performance of proteomics technologies is likely to have great impact on AD research.

AREAS COVERED

We review recent proteomics approaches that have advanced the field of clinical AD research. Specifically, we discuss the application of targeted mass spectrometry (MS), labeling-based and label-free MS-based as well as affinity-based proteomics to AD biomarker development, underpinning their importance with the latest impactful clinical studies. We evaluate how proteomics technologies have been adapted to meet current challenges. Finally, we discuss the limitations and potential of proteomics techniques and whether their scope might extend beyond current research-based applications.

EXPERT OPINION

To date, proteomics technologies in the AD field have been largely limited to AD biomarker discovery. The recent development of the first successful disease-modifying treatments of AD will further increase the need for blood biomarkers for early, accurate diagnosis, and CSF biomarkers that reflect specific pathological processes. Proteomics has the potential to meet these requirements and to progress into clinical routine practice, provided that current limitations are overcome.

Charting the Next Road Map for CSF Biomarkers in Alzheimer's Disease and Related Dementias

Clinical prediction of underlying pathologic substrates in people with Alzheimer's disease (AD) dementia or related dementia syndromes (ADRD) has limited accuracy. Etiologic biomarkers - including cerebrospinal fluid (CSF) levels of AD proteins and cerebral amyloid PET imaging - have greatly modernized disease-modifying clinical trials in AD, but their integration into medical practice has been slow. Beyond core CSF AD biomarkers (including beta-amyloid 1-42, total tau, and tau phosphorylated at threonine 181), novel biomarkers have been interrogated in single- and multi-centered studies with uneven rigor. Here, we review early expectations for ideal AD/ADRD biomarkers, assess these goals' future applicability, and propose study designs and performance thresholds for meeting these ideals with a focus on CSF biomarkers. We further propose three new characteristics: equity (oversampling of diverse populations in the design and testing of biomarkers), access (reasonable availability to 80% of people at risk for disease, along with pre- and post-biomarker processes), and reliability (thorough evaluation of pre-analytical and analytical factors influencing measurements and performance). Finally, we urge biomarker scientists to balance the desire and evidence for a biomarker to reflect its namesake function, indulge data- as well as theory-driven associations, re-visit the subset of rigorously measured CSF biomarkers in large datasets (such as Alzheimer's disease neuroimaging initiative), and resist the temptation to favor ease over fail-safe in the development phase. This shift from discovery to application, and from suspended disbelief to cogent ingenuity, should allow the AD/ADRD biomarker field to live up to its billing during the next phase of neurodegenerative disease research.

A MICROGLIAL ACTIVITY STATE BIOMARKER PANEL DIFFERENTIATES FTD-GRANULIN AND ALZHEIMER'S DISEASE PATIENTS FROM CONTROLS

Background

With the emergence of microglia-modulating therapies there is an urgent need for reliable biomarkers to evaluate microglial activation states.

Methods

Using mouse models and human induced pluripotent stem cell-derived microglia (hiMGL), which were genetically modified to yield the most opposite homeostatic ( TREM2- knockout) and disease-associated ( GRN -knockout) states, we identified microglia activity-dependent markers. Non-targeted mass spectrometry was used to identify changes in microglial and cerebrospinal (CSF) proteome of Grn - and Trem2 -knockout mice. Additionally, we analyzed the proteome of GRN - and TREM2 -knockout hiMGL and their conditioned media. Candidate marker proteins were tested in two independent patient cohorts, the ALLFTD cohort with 11 GRN mutation carriers and 12 non-carriers, as well as the proteomic data set available from the European Medical Information Framework Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD).

Findings

We identified proteomic changes between the opposite activation states in mouse microglia and cerebrospinal fluid (CSF), as well as in hiMGL cell lysates and conditioned media. For further verification, we analyzed the CSF proteome of heterozygous GRN mutation carriers suffering from frontotemporal dementia (FTD). We identified a panel of six proteins (FABP3, MDH1, GDI1, CAPG, CD44, GPNMB) as potential indicators for microglial activation. Moreover, we confirmed three of these proteins (FABP3, GDI1, MDH1) to be significantly elevated in the CSF of AD patients. In AD, these markers differentiated amyloid-positive cases with mild cognitive impairment (MCI) from amyloid-negative individuals.

Interpretation

The identified candidate proteins reflect microglia activity and may be relevant for monitoring the microglial response in clinical practice and clinical trials modulating microglial activity and amyloid deposition. Moreover, the finding that three of these markers differentiate amyloid-positive from amyloid-negative MCI cases in the AD cohort suggests that these marker proteins associate with a very early immune response to seeded amyloid. This is consistent with our previous findings in the DIAN (Dominantly Inherited Alzheimer's Disease Network) cohort, where soluble TREM2 increases as early as 21 years before symptom onset. Moreover, in mouse models for amyloidogenesis, seeding of amyloid is limited by physiologically active microglia further supporting their early protective role. The biological functions of some of our main candidates (FABP3, CD44, GPNMB) also further emphasize that lipid dysmetabolism may be a common feature of neurodegenerative disorders.

Funding

This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy - ID 390857198 to CH, SFL and DP) and a Koselleck Project HA1737/16-1 (to CH).

APOE and immunity: Research highlights

INTRODUCTION

At the Alzheimer's Association's APOE and Immunity virtual conference, held in October 2021, leading neuroscience experts shared recent research advances on and inspiring insights into the various roles that both the apolipoprotein E gene (APOE) and facets of immunity play in neurodegenerative diseases, including Alzheimer's disease and other dementias.

METHODS

The meeting brought together more than 1200 registered attendees from 62 different countries, representing the realms of academia and industry.

RESULTS

During the 4-day meeting, presenters illuminated aspects of the cross-talk between APOE and immunity, with a focus on the roles of microglia, triggering receptor expressed on myeloid cells 2 (TREM2), and components of inflammation (e.g., tumor necrosis factor α [TNFα]).

DISCUSSION

This manuscript emphasizes the importance of diversity in current and future research and presents an integrated view of innate immune functions in Alzheimer's disease as well as related promising directions in drug development.

Cerebrospinal fluid biomarker panel of synaptic dysfunction in Alzheimer's disease and other neurodegenerative disorders

INTRODUCTION

Synaptic degeneration is a key part of the pathophysiology of neurodegenerative diseases, and biomarkers reflecting the pathological alterations are greatly needed.

METHOD

Seventeen synaptic proteins were quantified in a pathology-confirmed cerebrospinal fluid cohort of patients with Alzheimer's disease (AD; n = 63), frontotemporal lobar degeneration (FTLD; n = 53), and Lewy body spectrum of disorders (LBD; n = 21), as well as healthy controls (HC; n = 48).

RESULTS

Comparisons revealed four distinct patterns: markers decreased across all neurodegenerative conditions compared to HC (the neuronal pentraxins), markers increased across all neurodegenerative conditions (14-3-3 zeta/delta), markers selectively increased in AD compared to other neurodegenerative conditions (neurogranin and beta-synuclein), and markers selectively decreased in LBD and FTLD compared to HC and AD (AP2B1 and syntaxin-1B).

DISCUSSION

Several of the synaptic proteins may serve as biomarkers for synaptic dysfunction in AD, LBD, and FTLD. Additionally, differential patterns of synaptic protein alterations seem to be present across neurodegenerative diseases.

HIGHLIGHTS

A panel of synaptic proteins were quantified in the cerebrospinal fluid using mass spectrometry. We compared Alzheimer's disease, frontotemporal degeneration, and Lewy body spectrum of disorders. Pathology was confirmed by autopsy or familial mutations. We discovered synaptic biomarkers for synaptic degeneration and cognitive decline. We found differential patterns of synaptic proteins across neurodegenerative diseases.

Towards a future where Alzheimer's disease pathology is stopped before the onset of dementia

Alzheimer's disease (AD) is a major healthcare challenge with no curative treatment at present. To address this challenge, we need a paradigm shift, where we focus on pre-dementia stages of AD. In this Perspective, we outline a strategy to move towards a future with personalized medicine for AD by preparing for and investing in effective and patient-orchestrated diagnosis, prediction and prevention of the dementia stage. While focusing on AD, this Perspective also discusses studies that do not specify the cause of dementia. Future personalized prevention strategies encompass multiple components, including tailored combinations of disease-modifying interventions and lifestyle. By empowering the public and patients to be more actively engaged in the management of their health and disease and by developing improved strategies for diagnosis, prediction and prevention, we can pave the way for a future with personalized medicine, in which AD pathology is stopped to prevent or delay the onset of dementia.

Discovery of novel CSF biomarkers to predict progression in dementia using machine learning

Providing an accurate prognosis for individual dementia patients remains a challenge since they greatly differ in rates of cognitive decline. In this study, we used machine learning techniques with the aim to identify cerebrospinal fluid (CSF) biomarkers that predict the rate of cognitive decline within dementia patients. First, longitudinal mini-mental state examination scores (MMSE) of 210 dementia patients were used to create fast and slow progression groups. Second, we trained random forest classifiers on CSF proteomic profiles and obtained a well-performing prediction model for the progression group (ROC-AUC = 0.82). As a third step, Shapley values and Gini feature importance measures were used to interpret the model performance and identify top biomarker candidates for predicting the rate of cognitive decline. Finally, we explored the potential for each of the 20 top candidates in internal sensitivity analyses. TNFRSF4 and TGF [Formula: see text]-1 emerged as the top markers, being lower in fast-progressing patients compared to slow-progressing patients. Proteins of which a low concentration was associated with fast progression were enriched for cell signalling and immune response pathways. None of our top markers stood out as strong individual predictors of subsequent cognitive decline. This could be explained by small effect sizes per protein and biological heterogeneity among dementia patients. Taken together, this study presents a novel progression biomarker identification framework and protein leads for personalised prediction of cognitive decline in dementia.

Rationale and design of the ABOARD project (A Personalized Medicine Approach for Alzheimer's Disease)

The key to stopping Alzheimer's disease (AD) lies in the pre-dementia stages, with the goal to stop AD before dementia has started. We present the rationale and design of the ABOARD (A Personalized Medicine Approach for Alzheimer's Disease) project, which aims to invest in personalized medicine for AD. ABOARD is a Dutch public-private partnership of 32 partners, connecting stakeholders from a scientific, clinical, and societal perspective. The 5-year project is structured into five work packages on (1) diagnosis, (2) prediction, (3) prevention, (4) patient-orchestrated care, and (5) communication and dissemination. ABOARD functions as a network organization in which professionals interact cross-sectorally. ABOARD has a strong junior training program "Juniors On Board." Project results are shared with society through multiple communication resources. By including relevant partners and involving citizens at risk, patients, and their care partners, ABOARD builds toward a future with personalized medicine for AD.

Highlights

ABOARD (A Personalized Medicine Approach for Alzheimer's Disease) is a public-private research project executed by 32 partners that functions as a network organization.Together, the project partners build toward a future with personalized medicine for Alzheimer's disease.Although ABOARD is a Dutch consortium, it has international relevance.ABOARD improves diagnosis, prediction, prevention, and patient-orchestrated care.

TMT proteomics analysis of cerebrospinal fluid from patients with cerebral venous sinus thrombosis

CVST is a type of venous stroke that mainly affects young adults with no reliable diagnostic markers and effective treatment strategies for secondary pathologies. However, the underlying pathological molecular mechanisms remain unclear. Here, we systematically analyzed the molecule profiling of the cerebrospinal fluid (CSF) in CVST patients via tandem mass tag (TMT)-based proteomics for the first time, aiming to reveal the pathogenesis and provide evidence for the diagnosis and treatment of CVST. Five CVST patients and five control patients were selected, and CSF samples were analyzed by TMT proteomics. Differentially expressed proteins (DEPs) were acquired and bioinformatics analysis was performed. Besides, parallel reaction monitoring (PRM) was utilized to validate the DEPs. 468 differentially expressed proteins were screened, 185 of which were up-regulated and 283 were down-regulated (fold change >1.2, P < 0.05). Bioinformatics analysis displayed that these proteins were significantly enriched in multiple pathways related to a variety of pathophysiological processes. PRM verification showed that apolipoprotein E, MMP-2, neuroserpin, clusterin, and several other molecules were down-regulated. These identified proteins reveal unique pathophysiological characteristics secondary to CVST. Further characterization of these proteins in future research could enable their application as potential therapeutic targets and biomarkers in CVST therapy. SIGNIFICANCE: Cerebral venous sinus thrombosis (CVST) is an underrated and potentially fatal cause of stroke with a reported mortality of 5-10% worldwide. Currently, in addition to anticoagulant and thrombolytic therapy, effective treatments targeting the injured brain parenchyma after CVST remain limited. Besides, accurate diagnostic markers are still sorely lacking. In the present study, we will detect the alterations of the CSF protein spectrum of CVST patients by TMT technique, screen differentially expressed proteins, analyze the functions of these signals through bioinformatics methods, and finally validate the key molecules through parallel reaction monitoring (PRM) technique. Collectively, the study aimed to offer a reference for the discovery of specific protein/pathway alterations in the CSF of CVST patients and further reveal the underlying pathogenesis, thereby providing evidence for the diagnosis and treatment of CVST.

Transferability of Alzheimer's disease progression subtypes to an independent population cohort

In the past, methods to subtype or biotype patients using brain imaging data have been developed. However, it is unclear whether and how these trained machine learning models can be successfully applied to population cohorts to study the genetic and lifestyle factors underpinning these subtypes. This work, using the Subtype and Stage Inference (SuStaIn) algorithm, examines the generalisability of data-driven Alzheimer's disease (AD) progression models. We first compared SuStaIn models trained separately on Alzheimer's disease neuroimaging initiative (ADNI) data and an AD-at-risk population constructed from the UK Biobank dataset. We further applied data harmonization techniques to remove cohort effects. Next, we built SuStaIn models on the harmonized datasets, which were then used to subtype and stage subjects in the other harmonized dataset. The first key finding is that three consistent atrophy subtypes were found in both datasets, which match the previously identified subtype progression patterns in AD: 'typical', 'cortical' and 'subcortical'. Next, the subtype agreement was further supported by high consistency in individuals' subtypes and stage assignment based on the different models: more than 92% of the subjects, with reliable subtype assignment in both ADNI and UK Biobank dataset, were assigned to an identical subtype under the model built on the different datasets. The successful transferability of AD atrophy progression subtypes across cohorts capturing different phases of disease development enabled further investigations of associations between AD atrophy subtypes and risk factors. Our study showed that (1) the average age is highest in the typical subtype and lowest in the subcortical subtype; (2) the typical subtype is associated with statistically more-AD-like cerebrospinal fluid biomarkers values in comparison to the other two subtypes; and (3) in comparison to the subcortical subtype, the cortical subtype subjects are more likely to associate with prescription of cholesterol and high blood pressure medications. In summary, we presented cross-cohort consistent recovery of AD atrophy subtypes, showing how the same subtypes arise even in cohorts capturing substantially different disease phases. Our study opened opportunities for future detailed investigations of atrophy subtypes with a broad range of early risk factors, which will potentially lead to a better understanding of the disease aetiology and the role of lifestyle and behaviour on AD.

Biomarkers of diagnosis, prognosis, pathogenesis, response to therapy: Convergence or divergence? Lessons from Alzheimer's disease and synucleinopathies

Alzheimer's disease (AD) is the most common disorder associated with cognitive impairment. Recent observations emphasize the pathogenic role of multiple factors inside and outside the central nervous system, supporting the notion that AD is a syndrome of many etiologies rather than a "heterogeneous" but ultimately unifying disease entity. Moreover, the defining pathology of amyloid and tau coexists with many others, such as α-synuclein, TDP-43, and others, as a rule, not an exception. Thus, an effort to shift our AD paradigm as an amyloidopathy must be reconsidered. Along with amyloid accumulation in its insoluble state, β-amyloid is becoming depleted in its soluble, normal states, as a result of biological, toxic, and infectious triggers, requiring a shift from convergence to divergence in our approach to neurodegeneration. These aspects are reflected-in vivo-by biomarkers, which have become increasingly strategic in dementia. Similarly, synucleinopathies are primarily characterized by abnormal deposition of misfolded α-synuclein in neurons and glial cells and, in the process, depleting the levels of the normal, soluble α-synuclein that the brain needs for many physiological functions. The soluble to insoluble conversion also affects other normal brain proteins, such as TDP-43 and tau, accumulating in their insoluble states in both AD and dementia with Lewy bodies (DLB). The two diseases have been distinguished by the differential burden and distribution of insoluble proteins, with neocortical phosphorylated tau deposition more typical of AD and neocortical α-synuclein deposition peculiar to DLB. We propose a reappraisal of the diagnostic approach to cognitive impairment from convergence (based on clinicopathologic criteria) to divergence (based on what differs across individuals affected) as a necessary step for the launch of precision medicine.

Sixteen-Year Longitudinal Evaluation of Blood-Based DNA Methylation Biomarkers for Early Prediction of Alzheimer's Disease

BACKGROUND

DNA methylation (DNAm), an epigenetic mark reflecting both inherited and environmental influences, has shown promise for Alzheimer's disease (AD) prediction.

OBJECTIVE

Testing long-term predictive ability (>15 years) of existing DNAm-based epigenetic age acceleration (EAA) measures and identifying novel early blood-based DNAm AD-prediction biomarkers.

METHODS

EAA measures calculated from Illumina EPIC data from blood were tested with linear mixed-effects models (LMMs) in a longitudinal case-control sample (50 late-onset AD cases; 51 matched controls) with prospective data up to 16 years before clinical onset, and post-onset follow-up. Novel DNAm biomarkers were generated with epigenome-wide LMMs, and Sparse Partial Least Squares Discriminant Analysis applied at pre- (10-16 years), and post-AD-onset time-points.

RESULTS

EAA did not differentiate cases from controls during the follow-up time (p > 0.05). Three new DNA biomarkers showed in-sample predictive ability on average 8 years pre-onset, after adjustment for age, sex, and white blood cell proportions (p-values: 0.022-<0.00001). Our longitudinally-derived panel replicated nominally (p = 0.012) in an external cohort (n = 146 cases, 324 controls). However, its effect size and discriminatory accuracy were limited compared to APOEɛ4-carriership (OR = 1.38 per 1 SD DNAm score increase versus OR = 13.58 for ɛ4-allele carriage; AUCs = 77.2% versus 87.0%). Literature review showed low overlap (n = 4) across 3275 AD-associated CpGs from 8 published studies, and no overlap with our identified CpGs.