CSF Proteomic Alzheimer's Disease-Predictive Subtypes in Cognitively Intact Amyloid Negative Individuals

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.

Signatures and Discriminative Abilities of Multi-Omics between States of Cognitive Decline

Dementia poses a substantial global health challenge, warranting an exploration of its intricate pathophysiological mechanisms and potential intervention targets. Leveraging multi-omic technology, this study utilizes data from 2251 participants to construct classification models using lipidomic, gut metabolomic, and cerebrospinal fluid (CSF) proteomic markers to distinguish between the states of cognitive decline, namely, the cognitively unimpaired state, mild cognitive impairment, and dementia. The analysis identifies three CSF proteins (apolipoprotein E, neuronal pentraxin-2, and fatty-acid-binding protein), four lipids (DEDE.18.2, DEDE.20.4, LPC.O.20.1, and LPC.P.18.1), and five serum gut metabolites (Hyodeoxycholic acid, Glycohyodeoxycholic acid, Hippuric acid, Glyceric acid, and Glycodeoxycholic acid) capable of predicting dementia prevalence from cognitively unimpaired participants, achieving Area Under the Curve (AUC) values of 0.879 (95% CI: 0.802-0.956), 0.766 (95% CI: 0.700-0.835), and 0.717 (95% CI: 0.657-0.777), respectively. Furthermore, exclusively three CSF proteins exhibit the potential to predict mild cognitive impairment prevalence from cognitively unimpaired subjects, with an AUC of 0.760 (95% CI: 0.691-0.828). In conclusion, we present novel combinations of lipids, gut metabolites, and CSF proteins that showed discriminative abilities between the states of cognitive decline and underscore the potential of these molecules in elucidating the mechanisms of cognitive decline.

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.

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.

How Many Alzheimer-Perusini's Atypical Forms Do We Still Have to Discover?

Alzheimer-Perusini's (AD) disease represents the most spread dementia around the world and constitutes a serious problem for public health. It was first described by the two physicians from whom it took its name. Nowadays, we have extensively expanded our knowledge about this disease. Starting from a merely clinical and histopathologic description, we have now reached better molecular comprehension. For instance, we passed from an old conceptualization of the disease based on plaques and tangles to a more modern vision of mixed proteinopathy in a one-to-one relationship with an alteration of specific glial and neuronal phenotypes. However, no disease-modifying therapies are yet available. It is likely that the only way to find a few "magic bullets" is to deepen this aspect more and more until we are able to draw up specific molecular profiles for single AD cases. This review reports the most recent classifications of AD atypical variants in order to summarize all the clinical evidence using several discrimina (for example, post mortem neurofibrillary tangle density, cerebral atrophy, or FDG-PET studies). The better defined four atypical forms are posterior cortical atrophy (PCA), logopenic variant of primary progressive aphasia (LvPPA), behavioral/dysexecutive variant and AD with corticobasal degeneration (CBS). Moreover, we discuss the usefulness of such classifications before outlining the molecular-genetic aspects focusing on microglial activity or, more generally, immune system control of neuroinflammation and neurodegeneration.

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.

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.

Blood biomarkers in mild cognitive impairment patients: Relationship between analytes and progression to Alzheimer disease dementia

BACKGROUND AND PURPOSE

Blood-based biomarkers are promising tools for the diagnosis of Alzheimer disease (AD) at prodromal stages (mild cognitive impairment [MCI]) and are hoped to be implemented as screening tools for patients with cognitive complaints. In this work, we evaluated the potential of peripheral neurological biomarkers to predict progression to AD dementia and the relation between blood and cerebrospinal fluid (CSF) AD markers in MCI patients referred from a general neurological department.

METHODS

A group of 106 MCI patients followed at the Neurology Department of Coimbra University Hospital was included. Data regarding baseline neuropsychological evaluation, CSF levels of amyloid β 42 (Aβ42), Aβ40, total tau (t-Tau), and phosphorylated tau 181 (p-Tau181) were available for all the patients. Aβ42, Aβ40, t-Tau, p-Tau181, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) levels were determined in baseline stored serum and plasma samples by commercial SiMoA (Single Molecule Array) assays. Progression from MCI to AD dementia was assessed at follow-up (mean = 5.8 ± 3.4 years).

RESULTS

At baseline, blood markers NfL, GFAP, and p-Tau181 were significantly increased in patients who progressed to AD at follow-up (p < 0.001). In contrast, plasma Aβ42/40 ratio and t-Tau showed no significant differences between groups. NfL, GFAP, and p-Tau181 demonstrated good diagnostic accuracy to identify progression to AD dementia (area under the curve [AUC] = 0.81, 0.80, and 0.76, respectively), which improved when combined (AUC = 0.89). GFAP and p-Tau181 were correlated with CSF Aβ42. Association of p-Tau181 with NfL was mediated by GFAP, with a significant indirect association of 88% of the total effect.

CONCLUSIONS

Our findings highlight the potential of combining blood-based GFAP, NfL, and p-Tau181 to be applied as a prognostic tool in MCI.

CSF-Targeted Proteomics Indicate Amyloid-Beta Ratios in Patients with Alzheimer's Dementia Spectrum

Background

According to recent studies, amyloid-β (Aβ) isoforms as cerebrospinal fluid (CSF) biomarkers have remarkable predictive value for cognitive decline in the early stages of Alzheimer's disease (AD). Herein, we aimed to investigate the correlations between several targeted proteomics in CSF samples with Aβ ratios and cognitive scores in patients in AD spectrum to search for potential early diagnostic utility.

Methods

A total of 719 participants were found eligible for inclusion. Patients were then categorized into cognitively normal (CN), mild cognitive impairment (MCI), and AD and underwent an assessment of Aβ and proteomics. Clinical Dementia Rating (CDR), Alzheimer's Disease Assessment Scale (ADAS), and Mini Mental State Exam (MMSE) were used for further cognitive assessment. The Aβ42, Aβ42/Aβ40, and Aβ42/38 ratios were considered as means of comparison to identify those peptides corresponding significantly to these established biomarkers and cognitive scores. The diagnostic utility of the IASNTQSR, VAELEDEK, VVSSIEQK, GDSVVYGLR, EPVAGDAVPGPK, and QETLPSK was assessed.

Results

All investigated peptides corresponded significantly to Aβ42 in controls. In those with MCI, VAELEDEK and EPVAGDAVPGPK were significantly correlated with Aβ42 (p value < 0.001). Additionally, IASNTQSR, VVSSIEQK, GDSVVYGLR, and QETLPSK were significantly correlated with Aβ42/Aβ40 and Aβ42/38 (p value < 0.001) in this group. This group of peptides similarly corresponded to Aβ ratios in those with AD. Eventually, IASNTQSR, VAELEDEK, and VVSSIEQK were significantly associated with CDR, ADAS-11, and ADAS-13, particularly in MCI group.

Conclusion

Our research suggests potential early diagnostic and prognostic utilities for certain peptides extracted from CSF-targeted proteomics research. The ethical approval of ADNI is available at ClinicalTrials.gov with Identifier: NCT00106899.