Why Inclusion Matters for Alzheimer's Disease Biomarker Discovery in Plasma

Alzheimer's disease CSF biomarkers correlate with early pathology and alterations in neuronal and glial gene expression

INTRODUCTION

Normal pressure hydrocephalus (NPH) patients undergoing cortical shunting frequently show early Alzheimer's disease (AD) pathology on cortical biopsy, which is predictive of progression to clinical AD. The objective of this study was to use samples from this cohort to identify cerebrospinal fluid  (CSF) biomarkers for AD-related central nervous system (CNS) pathophysiologic changes using tissue and fluids with early pathology, free of post mortem artifact.

METHODS

We analyzed Simoa, proteomic, and metabolomic CSF data from 81 patients with previously documented pathologic and transcriptomic changes.

RESULTS

AD pathology on biopsy correlates with CSF β-amyloid-42/40, neurofilament light chain (NfL), and phospho-tau-181(p-tau181)/β-amyloid-42, while several gene expression modules correlate with NfL. Proteomic analysis highlights seven core proteins that correlate with pathology and gene expression changes on biopsy, and metabolomic analysis of CSF identifies disease-relevant groups that correlate with biopsy data.

DISCUSSION

As additional biomarkers are added to AD diagnostic panels, our work provides insight into the CNS pathophysiology these markers are tracking.

HIGHLIGHTS

AD CSF biomarkers correlate with CNS pathology and transcriptomic changes. Seven proteins correlate with CNS pathology and gene expression changes. Inflammatory and neuronal gene expression changes correlate with YKL-40 and NPTXR, respectively. CSF metabolomic analysis identifies pathways that correlate with biopsy data. Fatty acid metabolic pathways correlate with β-amyloid pathology.

Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.

Clinical application of plasma P-tau217 to assess eligibility for amyloid-lowering immunotherapy in memory clinic patients with early Alzheimer's disease

BACKGROUND

With the approval of disease-modifying treatments (DMTs) for early Alzheimer's disease (AD), there is an increased need for efficient and non-invasive detection methods for cerebral amyloid-β (Aβ) pathology. Current methods, including positron emission tomography (PET) and cerebrospinal fluid (CSF) analysis, are costly and invasive methods that may limit access to new treatments. Plasma tau phosphorylated at threonine-217 (P-tau217) presents a promising alternative, yet optimal cutoffs for treatment eligibility with DMTs like aducanumab require further investigation. This study evaluates the efficacy of one- and two-cutoff strategies for determining DMT eligibility at the Butler Hospital Memory & Aging Program (MAP).

METHODS

In this retrospective, cross-sectional diagnostic cohort study, we first developed P-tau217 cutoffs using site-specific and BioFINDER-2 training data, which were then tested in potential DMT candidates from Butler MAP (total n = 150). ROC analysis was used to calculate the area under the curve (AUC) and accuracy of P-tau217 interpretation strategies, using Aβ-PET/CSF testing as the standard of truth.

RESULTS

Potential DMT candidates at Butler MAP (n = 50), primarily diagnosed with mild cognitive impairment (n = 29 [58%]) or mild dementia (21 [42%]), were predominantly Aβ-positive (38 [76%]), and half (25 [50%]) were subsequently treated with aducanumab. Elevated P-tau217 predicted cerebral Aβ positivity in potential DMT candidates (AUC = 0.97 [0.92-1]), with diagnostic accuracy ranging from 0.88 (0.76-0.95, p = 0.028) to 0.96 (0.86-1, p < .001). When using site-specific cutoffs, a subset of DMT candidates (10%) exhibited borderline P-tau217 (between 0.273 and 0.399 pg/mL) that would have potentially required confirmatory testing.

CONCLUSIONS

This study, which included participants treated with aducanumab, confirms the utility of one- and two-cutoff strategies for interpreting plasma P-tau217 in assessing DMT eligibility. Using P-tau217 could potentially replace more invasive diagnostic methods, and all aducanumab-treated participants would have been deemed eligible based on P-tau217. However, false positives remain a concern, particularly when applying externally derived cutoffs that exhibited lower specificity which could have led to inappropriate treatment of Aβ-negative participants. Future research should focus on prospective validation of P-tau217 cutoffs to enhance their generalizability and inform standardized treatment decision-making across diverse populations.

Alzheimer's disease CSF biomarkers correlate with early pathology and alterations in neuronal and glial gene expression

INTRODUCTION

Normal pressure hydrocephalus (NPH) patients undergoing cortical shunting frequently show early AD pathology on cortical biopsy, which is predictive of progression to clinical AD. The objective of this study was to use samples from this cohort to identify CSF biomarkers for AD-related CNS pathophysiologic changes using tissue and fluids with early pathology, free of post-mortem artifact.

METHODS

We analyzed Simoa, proteomic, and metabolomic CSF data from 81 patients with previously documented pathologic and transcriptomic changes.

RESULTS

AD pathology on biopsy correlates with CSF β-amyloid-40/42, neurofilament light chain (NfL), and phospho-tau-181(p-tau181)/β-amyloid-42, while several gene expression modules correlate with NfL. Proteomic analysis highlights 7 core proteins that correlate with pathology and gene expression changes on biopsy, and metabolomic analysis of CSF identifies disease-relevant groups that correlate with biopsy data..

DISCUSSION

As additional biomarkers are added to AD diagnostic panels, our work provides insight into the CNS pathophysiology these markers are tracking.

Clinical application of plasma P-tau217 to assess eligibility for amyloid-lowering immunotherapy in memory clinic patients with early Alzheimer's disease

Background

With the approval of disease-modifying treatments (DMTs) for early Alzheimer's disease (AD), there is an increased need for efficient and non-invasive detection methods for cerebral amyloid-β (Aβ) pathology. Current methods, including positron emission tomography (PET) and cerebrospinal fluid (CSF) analysis, are costly and invasive methods that may limit access to new treatments. Plasma tau phosphorylated at threonine-217 (P-tau217) presents a promising alternative, yet optimal cutoffs for treatment eligibility with DMTs like aducanumab require further investigation. This study evaluates the efficacy of one- and two-cutoff strategies for determining DMT eligibility at the Butler Hospital Memory & Aging Program (MAP).

Methods

In this retrospective, cross-sectional diagnostic cohort study, we first developed P-tau217 cutoffs using site-specific training data and BioFINDER-2, which were then tested in potential DMT candidates from Butler MAP (total n = 150). ROC analysis was used to calculate the area under the curve (AUC) and accuracy of P-tau217 interpretation strategies, using Aβ-PET/CSF testing as the standard of truth.

Results

Potential DMT candidates at Butler MAP (n = 50), primarily diagnosed with mild cognitive impairment (n = 29 [58%]) or mild dementia (21 [42%]), were predominantly Aβ-positive (38 [76%]), and half (25 [50%]) were subsequently treated with aducanumab. Elevated P-tau217 predicted cerebral Aβ positivity in potential DMT candidates (AUC = 0.97 [0.92-1]), with diagnostic accuracy ranging from 0.88 (0.76-0.95, p = 0.028) to 0.96 (0.86-1, p < .001). When using site-specific cutoffs, a subset of DMT candidates (10%) exhibited borderline P-tau217 (between 0.273 and 0.399 pg/mL) that would have potentially required from confirmatory testing.

Conclusions

This study, which included participants treated with aducanumab, confirms the utility of one- and two-cutoff strategies for interpreting plasma P-tau217 in assessing DMT eligibility. Using P-tau217 could potentially replace more invasive diagnostic methods, and all aducanumab-treated participants would have been deemed eligible based on P-tau217. However, false positives remain a concern, particularly when applying externally derived cutoffs that exhibited lower specificity which could have led to inappropriate treatment of Aβ-negative participants. Future research should focus on prospective validation of P-tau217 cutoffs to enhance their generalizability and inform standardized treatment decision-making across diverse populations.

A blood-based multi-pathway biomarker assay for early detection and staging of Alzheimer's disease across ethnic groups

INTRODUCTION

Existing blood-based biomarkers for Alzheimer's disease (AD) mainly focus on its pathological features. However, studies on blood-based biomarkers associated with other biological processes for a comprehensive evaluation of AD status are limited.

METHODS

We developed a blood-based, multiplex biomarker assay for AD that measures the levels of 21 proteins involved in multiple biological pathways. We evaluated the assay's performance for classifying AD and indicating AD-related endophenotypes in three independent cohorts from Chinese or European-descent populations.

RESULTS

The 21-protein assay accurately classified AD (area under the receiver operating characteristic curve [AUC] = 0.9407 to 0.9867) and mild cognitive impairment (MCI; AUC = 0.8434 to 0.8945) while also indicating brain amyloid pathology. Moreover, the assay simultaneously evaluated the changes of five biological processes in individuals and revealed the ethnic-specific dysregulations of biological processes upon AD progression.

DISCUSSION

This study demonstrated the utility of a blood-based, multi-pathway biomarker assay for early screening and staging of AD, providing insights for patient stratification and precision medicine.

HIGHLIGHTS

The authors developed a blood-based biomarker assay for Alzheimer's disease. The 21-protein assay classifies AD/MCI and indicates brain amyloid pathology. The 21-protein assay can simultaneously assess activities of five biological processes. Ethnic-specific dysregulations of biological processes in AD were revealed.

Comprehensive Overview of Bottom-Up Proteomics using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods to aid the novice and experienced researcher. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this work to serve as a basic resource for new practitioners in the field of shotgun or bottom-up proteomics.

Demographic and Clinical Characteristics Associated With Serum GFAP Levels in an Ethnically Diverse Cohort

BACKGROUND AND OBJECTIVES

Elevations in circulating glial fibrillary acidic protein (GFAP), a putative marker of reactive astrocytosis, have been found to associate with cognitive decline and dementia status. Further validation in diverse cohorts and evaluation of potential health disparities are necessary for broader generalization. The goal of this study was to examine the associations between demographics, cardiovascular risk factors, and APOE ε4 status with serum GFAP levels among Mexican American and non-Hispanic White older adults across the continuum from cognitively unimpaired to Alzheimer disease dementia.

METHODS

Serum GFAP levels were assayed using a Simoa HD-1 analyzer in older adults enrolled in the observational Texas Alzheimer Research and Care Consortium. Associations between demographic and clinical characteristics with serum GFAP levels were evaluated using linear regression. The diagnostic accuracy of serum GFAP was further examined using area under the receiver operating characteristic curves (AUROC) in univariate and adjusted models, and optimal cut points were derived using the maximum Kolmogorov-Smirnov metric. All models were also stratified by ethnicity and disease stage.

RESULTS

A total of 1,156 Mexican American and 587 non-Hispanic White participants were included (mean age = 68 years, standard deviation = 10; 65% female). Older age (β = 0.562 (95% CI 0.515-0.609), p < 0.001), apolipoprotein ε4 status (β = 0.139 (95% CI 0.092-0.186), p < 0.001), and cognitive impairment (β = 0.150 (95% CI 0.103-0.197), p < 0.001) were positively associated with serum GFAP. By contrast, higher body mass index (β = -0.181 (95% CI -0.228 to -0.134), p < 0.001), diabetes (β = -0.065 (95% CI -0.112 to -0.018), p < 0.001), and tobacco use (β = -0.059 (95% CI -0.106 to -0.012), p < 0.001) were inversely associated with serum GFAP. AUROC values were generally comparable across ethnicities and model fit improved with inclusion of additional covariates. However, optimal cut-off values were consistently lower in Mexican Americans relative to non-Hispanic White participants.

DISCUSSION

The study results highlight the importance of understanding the role of broader demographic and clinical factors on circulating GFAP levels within diverse cohorts to enhance precision across clinical, research, and community settings.

Quantification of race/ethnicity representation in Alzheimer's disease neuroimaging research in the USA: a systematic review

BACKGROUND

Racial and ethnic minoritized groups are disproportionately at risk for Alzheimer's Disease (AD), but are not sufficiently recruited in AD neuroimaging research in the United States. This is important as sample composition impacts generalizability of findings, biomarker cutoffs, and treatment effects. No studies have quantified the breadth of race/ethnicity representation in the AD literature.

METHODS

This review identified median race/ethnicity composition of AD neuroimaging US-based research samples available as free full-text articles on PubMed. Two types of published studies were analyzed: studies that directly report race/ethnicity data (i.e., direct studies), and studies that do not report race/ethnicity but used data from a cohort study/database that does report this information (i.e., indirect studies).

RESULTS

Direct studies (n = 719) have median representation of 88.9% white or 87.4% Non-Hispanic white, 7.3% Black/African American, and 3.4% Hispanic/Latino ethnicity, with 0% Asian American, Native Hawaiian/Pacific Islander, and American Indian/Alaska Native, Multiracial, and Other Race participants. Cohort studies/databases (n = 44) from which indirect studies (n = 1745) derived are more diverse, with median representation of 84.2% white, 83.7% Non-Hispanic white, 11.6% Black/African American, 4.7% Hispanic/Latino, and 1.75% Asian American participants. Notably, 94% of indirect studies derive from just 10 cohort studies/databases. Comparisons of two time periods using a median split for publication year, 1994-2017 and 2018-2022, indicate that sample diversity has improved recently, particularly for Black/African American participants (3.39% from 1994-2017 and 8.29% from 2018-2022).

CONCLUSIONS

There is still underrepresentation of all minoritized groups relative to Census data, especially for Hispanic/Latino and Asian American individuals. The AD neuroimaging literature will benefit from increased representative recruitment of ethnic/racial minorities. More transparent reporting of race/ethnicity data is needed.

The Burden of Dementia Spectrum Disorders and Associated Comorbid and Demographic Features

Dementia spectrum disorders (DSDs) are a major cause of mortality and disability worldwide. DSDs encompass a large group of medical conditions that all ultimately lead to major functional and cognitive decline and disability. Demographic and comorbid conditions that are associated with DSDs have significant prognostic and preventive implications. In this article, we will discuss the global and regional burden of DSDs and cover key demographic and clinical conditions linked with DSDs. In the absence of disease-modifying treatments, the role of primary prevention has become more prominent. Implementation of preventive measures requires an understanding of predisposing and exacerbating factors.

Advances, obstacles, and opportunities for machine learning in proteomics

The fields of proteomics and machine learning are both large disciplines, each producing well over 5,000 publications per year. However, studies combining both fields are still relatively rare, with only about 2% of recent proteomics papers including machine learning. This review, which focuses on the intersection of the fields, is intended to inspire proteomics researchers to develop skills and knowledge in the application of machine learning. A brief tutorial introduction to machine learning is provided, and research advances that rely on both fields, particularly as they relate to proteomics tools development and biomarker discovery, are highlighted. Key knowledge gaps and opportunities for scientific advancement are also enumerated.

Moving beyond disclosure: Stages of care in preclinical Alzheimer's disease biomarker testing

Alzheimer's disease (AD) begins with an asymptomatic "preclinical" phase, in which abnormal biomarkers indicate risk for developing cognitive impairment. Biomarker information is increasingly being disclosed in research settings, and is moving toward clinical settings with the development of cheaper and non-invasive testing. Limited research has focused on the safety and psychological effects of disclosing biomarker results to cognitively unimpaired adults. However, less is known about how to ensure equitable access and robust counseling for decision-making before testing, and how to effectively provide long-term follow-up and risk management after testing. Using the framework of Huntington's disease, which is based on extensive experience with disclosing and managing risk for a progressive neurodegenerative condition, this article proposes a conceptual model of pre-disclosure, disclosure, and post-disclosure phases for AD biomarker testing. Addressing research questions in each phase will facilitate the transition of biomarker testing into clinical practice.

Characterizing Plasma Biomarkers of Alzheimer's in a Diverse Community-Based Cohort: A Cross-Sectional Study of the HAB-HD Cohort

Background

Due to their low cost, less invasive nature, and ready availability, plasma biomarkers of Alzheimer's disease have been proposed as one-time screening tools for clinical trials and research. The impact of ethnoracial factors on these biomarkers has received little attention. The current cross-sectional study investigated the levels of Aβ₄₀, Aβ₄₂, total tau (t tau), and neurofilament light (NfL) across diagnoses for each of the three major ethnoracial groups in the United States in a community-based cohort of older adults.

Methods

A total of 1,862 participants (852 Mexican Americans (MAs); 775 non-Hispanic Whites (NHWs), and 235 African Americans (AAs)) drawn from The Health & Aging Brain Study—Health Disparities (HABS-HD) study were included. Diagnoses were assigned using an algorithm (decision tree) verified by consensus review. Plasma samples were assayed using Simoa technology. Levels of each biomarker were compared for the three ethnoracial groups across cognitive diagnoses using ANOVA covarying sex and age.

Results

Significant differences were found across the groups at each level of cognitive impairment. Cognitively unimpaired (CU) AA had significantly lower levels of each of the biomarkers than cognitively unimpaired MA or NHW and NHW had higher levels of Aβ₄₀, and NfL than the other two groups. MA had higher t tau than AA or NHW. Mild cognitive impairment (MCI) group NHW had the highest levels on all the biomarkers and AA had the lowest. NHW and MA have higher levels of Aβ₄₀, Aβ₄₂, and t tau there was no difference between the groups for Aβ₄₂. NHW had significantly higher levels of Aβ₄₀, t tau, and NfL than AA. AA had a higher Aβ₄₂/Aβ₄₀ ratio than either NHW or MA for CU MCI.

Conclusions

The use of plasma biomarkers of cognitive decline is promising given their advantages over other biomarkers such as CSF and imaging but as the current research shows, ethnoracial differences must be considered to enhance accuracy and utility. Developing ethnoracial-specific cut points and establishing normative ranges by assay platform for each of the biomarkers are needed. Longitudinal research to assess changes in biomarkers during a cognitive decline is ongoing.

Prognosis of Alzheimer's Disease Using Quantitative Mass Spectrometry of Human Blood Plasma Proteins and Machine Learning

Early recognition of the risk of Alzheimer's disease (AD) onset is a global challenge that requires the development of reliable and affordable screening methods for wide-scale application. Proteomic studies of blood plasma are of particular relevance; however, the currently proposed differentiating markers are poorly consistent. The targeted quantitative multiple reaction monitoring (MRM) assay of the reported candidate biomarkers (CBs) can contribute to the creation of a consistent marker panel. An MRM-MS analysis of 149 nondepleted EDTA-plasma samples (MHRC, Russia) of patients with AD (n = 47), mild cognitive impairment (MCI, n = 36), vascular dementia (n = 8), frontotemporal dementia (n = 15), and an elderly control group (n = 43) was performed using the BAK 125 kit (MRM Proteomics Inc., Canada). Statistical analysis revealed a significant decrease in the levels of afamin, apolipoprotein E, biotinidase, and serum paraoxonase/arylesterase 1 associated with AD. Different training algorithms for machine learning were performed to identify the protein panels and build corresponding classifiers for the AD prognosis. Machine learning revealed 31 proteins that are important for AD differentiation and mostly include reported earlier CBs. The best-performing classifiers reached 80% accuracy, 79.4% sensitivity and 83.6% specificity and were able to assess the risk of developing AD over the next 3 years for patients with MCI. Overall, this study demonstrates the high potential of the MRM approach combined with machine learning to confirm the significance of previously identified CBs and to propose consistent protein marker panels.

Exposing the Brain Proteomic Signatures of Alzheimer's Disease in Diverse Racial Groups: Leveraging Multiple Data Sets and Machine Learning

Recent studies have highlighted that the proteome can be used to identify potential biomarker candidates for Alzheimer's disease (AD) in diverse cohorts. Furthermore, the racial and ethnic background of participants is an important factor to consider to ensure the effectiveness of potential biomarkers for representative populations. A promising approach to survey potential biomarker candidates for diagnosing AD in diverse cohorts is the application of machine learning to proteomics data sets. Herein, we leveraged six existing bottom-up proteomics data sets, which included non-Hispanic White, African American/Black, and Hispanic participants, to study protein changes in AD and cognitively unimpaired participants. Machine learning models were applied to these data sets and resulted in the identification of amyloid-β precursor protein (APP) and heat shock protein β-1 (HSPB1) as two proteins that have high ability to distinguish AD; however, each protein's performance varied based upon the racial and ethnic background of the participants. HSPB1 particularly was helpful for generating high areas under the curve (AUCs) for African American/Black participants. Overall, HSPB1 improved the performance of the machine learning models when combined with APP and/or participant age and is a potential candidate that should be further explored in AD biomarker discovery efforts.

Transcript levels in plasma contribute substantial predictive value as potential Alzheimer's disease biomarkers in African Americans

Background

African Americans (AA) remain underrepresented in Alzheimer's disease (AD) research, despite the prevalence of AD being double in AA compared to non-Hispanic whites. To address this disparity, our group has established the Florida Consortium for African American Alzheimer's Disease Studies (FCA³DS), focusing on the identification of genetic risk factors and novel plasma biomarkers.

Method

Utilizing FCA³DS whole exome sequence (WES) and plasma RNA samples from AD cases (n=151) and cognitively unimpaired (CU) elderly controls (n=269), we have performed differential gene expression (DGE) and expression quantitative trait locus (eQTL) analyses on 50 transcripts measured with a custom nanoString® panel. We designed this panel to measure, in plasma, cell-free mRNA (cf-mRNA) levels of AD-relevant genes.

Findings

Association with higher plasma CLU in CU vs. AD remained significant after Bonferroni correction. Study-wide significant eQTL associations were observed with 105 WES variants in cis with 22 genes, including variants in genes previously associated with AD risk in AA such as ABCA7 and AKAP9. Results from this plasma eQTL analysis identified AD-risk variants in ABCA7 and AKAP9 that are significantly associated with lower and higher plasma mRNA levels of these genes, respectively. Receiver operating characteristic analysis of age, sex APOE-ε4 dosage, CLU, APP, CD14, ABCA7, AKAP9 and APOE mRNA levels, and ABCA7 and AKAP9 eQTLs, achieved 77% area under the curve to discriminate AD vs. CU, an 8% improvement over a model that only included age, sex and APOE-ε4 dosage.

Interpretation

Incorporating plasma mRNA levels could contribute to improved predictive value of AD biomarker panels.

Funding

This work was supported by the National Institute on Aging [RF AG051504, U01 AG046139, R01 AG061796 to NET; P30 AG062677 to JAL and NGR]; Florida Health Ed and Ethel Moore Alzheimer's Disease grants [5AZ03 and 7AZ17 to NET; 7AZ07 to MMC; 8AZ08 to JAL].

Targeted Lipidomics To Measure Phospholipids and Sphingomyelins in Plasma: A Pilot Study To Understand the Impact of Race/Ethnicity in Alzheimer's Disease

The number of people suffering from Alzheimer's disease (AD) is increasing rapidly every year. One aspect of AD that is often overlooked is the disproportionate incidence of AD among African American/Black populations. With the recent development of novel assays for lipidomics analysis in recent times, there has been a drastic increase in the number of studies focusing on changes of lipids in AD. However, very few of these studies have focused on or even included samples from African American/Black individuals samples. In this study, we aimed to determine if the lipidome in AD is universal across non-Hispanic White and African American/Black individuals. To accomplish this, a targeted mass spectrometry lipidomics analysis was performed on plasma samples (N = 113) obtained from cognitively normal (CN, N = 54) and AD (N = 59) individuals from African American/Black (N = 56) and non-Hispanic White (N = 57) backgrounds. Five lipids (PS 18:0_18:0, PS 18:0_20:0, PC 16:0_22:6, PC 18:0_22:6, and PS 18:1_22:6) were altered between AD and CN sample groups (p value < 0.05). Upon racial stratification, there were notable differences in lipids that were unique to African American/Black or non-Hispanic White individuals. PS 20:0_20:1 was reduced in AD in samples from non-Hispanic White but not African American/Black adults. We also tested whether race/ethnicity significantly modified the association between lipids and AD status by including a race × diagnosis interaction term in a linear regression model. PS 20:0_20:1 showed a significant interaction (p = 0.004). The discovery of lipid changes in AD in this study suggests that identifying relevant lipid biomarkers for diagnosis will require diversity in sample cohorts.

What Influences the Willingness of Blacks and African Americans to Enroll in Preclinical Alzheimer's Disease Biomarker Research? A Qualitative Vignette Analysis

BACKGROUND

Alzheimer's disease (AD) begins with an asymptomatic "preclinical" phase, in which abnormal biomarkers indicate risk for developing cognitive impairment. Research is increasingly focused on validating biomarkers to improve reliable diagnosis and timely clinical treatment of AD. Most preclinical biomarker research lacks adequate representation of Black/African American and other racially and ethnically minoritized individuals, limiting the applicability of data to these groups. This may exacerbate existing disparities by hindering diagnosis and treatment among racially and ethnically minoritized individuals.

OBJECTIVE

Understand the factors influencing willingness of Blacks/African Americans to participate in AD biomarker research and identify opportunities to improve enrollment.

METHODS

We enrolled Blacks/African Americans (N = 145) between 46-85 years of age who had previously participated in AD research. Participants gave open-ended responses to a vignette describing a hypothetical biomarker research study. Using qualitative content analysis, we identified themes that motivated and discouraged enrollment in AD biomarker research.

RESULTS

Participant responses were categorized into several themes. Themes motivating participation included a desire to know their biomarker results and to support research. Major themes discouraging participation included concerns about potential negative psychological outcomes to learning one's increased risk for AD, doubt about the usefulness of testing, and worry about the potential physical harms of testing.

CONCLUSION

Understanding themes motivating and discouraging AD preclinical biomarker research participation may inform research material development, approach to community engagement, and/or trial design to increase enrollment of Blacks/African Americans.

Improved Discrimination of Disease States Using Proteomics Data with the Updated Aristotle Classifier

Mass spectrometry data sets from omics studies are an optimal information source for discriminating patients with disease and identifying biomarkers. Thousands of proteins or endogenous metabolites can be queried in each analysis, spanning several orders of magnitude in abundance. Machine learning tools that effectively leverage these data to accurately identify disease states are in high demand. While mass spectrometry data sets are rich with potentially useful information, using the data effectively can be challenging because of missing entries in the data sets and because the number of samples is typically much smaller than the number of features, two challenges that make machine learning difficult. To address this problem, we have modified a new supervised classification tool, the Aristotle Classifier, so that omics data sets can be better leveraged for identifying disease states. The optimized classifier, AC.2021, is benchmarked on multiple data sets against its predecessor and two leading supervised classification tools, Support Vector Machine (SVM) and XGBoost. The new classifier, AC.2021, outperformed existing tools on multiple tests using proteomics data. The underlying code for the classifier, provided herein, would be useful for researchers who desire improved classification accuracy when using their omics data sets to identify disease states.

Dataset of why inclusion matters for Alzheimer's disease biomarker discovery in plasma

Here we present a plasma proteomics dataset that was generated to understand the importance of self-reported race for biomarker discovery in Alzheimer's disease. This dataset is related to the article "Why inclusion matters for Alzheimer's disease biomarker discovery in plasma" [1]. Plasma samples were obtained from clinically diagnosed Alzheimer's disease and cognitively normal adults of African American/Black and non-Hispanic White racial and ethnic backgrounds. Plasma was immunodepleted, digested, and isobarically tagged with commercial reagents. Tagged peptides were fractionated using high pH fractionation and resulting fractions analysed by liquid chromatography - mass spectrometry (LC-MS/MS & MS3) analysis on an Orbitrap Fusion Lumos mass spectrometer. The resulting data was processed using Proteome Discoverer to produce a list of identified proteins with corresponding tandem mass tag (TMT) intensity information.