Kuchenbecker, Lindsey A.

Emerging evidence suggests that systemic physiological factors may influence plasma biomarker concentrations of Alzheimer’s disease (AD) and related neurodegenerative processes, potentially affecting their specificity for central nervous system pathology. This study examined the relationship of demographic factors and medical comorbidities with plasma biomarkers of AD and neurodegeneration in a community-dwelling cohort of Black/African American (B/AA) older adults (N = 141). Participants underwent plasma assessment of phosphorylated tau at threonine 217 (p-Tau217), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). Results showed associations between plasma p-Tau217 and amyloid PET positivity, and significant intercorrelations among p-Tau217, GFAP, and NfL. Stepwise regression models incorporated demographics, amyloid PET status, and laboratory measures of renal, metabolic, and lipid function as predictors for each biomarker. p-Tau217 was primarily predicted by amyloid PET and renal function; GFAP by age and sex; and NfL by renal function, age, and sex. Findings indicate plasma biomarker concentrations in B/AA older adults reflect both central AD-related pathology and systemic physiological factors, particularly renal function, and demographic influences. Results underscore the importance of accounting for comorbid medical conditions and demographic characteristics when interpreting blood-based biomarkers and highlight the need for comprehensive medical phenotyping to improve diagnostic specificity and clinical utility.

Abstract Background African Americans (AA) are twice as likely to develop dementia than non‐Hispanic Whites but remain underrepresented in Alzheimer's Disease (AD) research. Recent studies have identified AA specific risk factors for AD. Therefore, targeted biomarkers and/or therapies may be needed to effectively treat AD in AA. In this study, we aimed to determine if genetic variants that show association with AD‐risk, plasma transcript or protein levels in AA, may serve as accurate and accessible AD biomarkers. Method In 189 clinically diagnosed AA AD cases and 183 AA cognitively unimpaired (CU) controls, we performed targeted DNA sequencing of 10 AD‐associated loci. Genetic variants at these loci were tested for association with corresponding plasma transcripts and total tau protein levels previously measured in this cohort using a custom nanoString panel and Simoa assays, respectively. Utilizing phased haplotypes (SHAPEIT4) from target sequencing, we inferred local ancestry (RFMix v2) using five superpopulations from the 1000 Genomes Project as anchors. Subsequently, we tested the association of each ancestry specific allelic dosages with endophenotypes using Tractor. In receiver operating characteristic (ROC) analyses, the most significant e/pQTLs were added sequentially to a base model that included age and sex to identify e/pQTLs that improved the accuracy to correctly classify AD cases and CU controls. Result Of the 5,112 variants that were tested, 70 were nominally associated with AD‐risk and with plasma transcript or protein levels. Seven of these 70 variants also showed nominally significant associations of AD‐risk and transcript levels with either African or East Asian local ancestry. ROC analysis of age, sex, 33 e/pQTLs in APOE , SORL1 , TLR4 , EPHA1 , ABCA7 , CLU , and CR1 , and plasma levels of APP, ABCA7, AKAP9, CD14, CLU, and APOE ‐ɛ4 dosage achieved 88.2% area under the curve to discriminate AD vs. CU, a 30.4% improvement over the base model that only included age and sex. Conclusion Plasma transcript levels and e/pQTLs are promising diagnostic biomarkers for AD that may improve accessibility and reduce costs for a more accurate diagnosis of AD.

Abstract Background Clinicians’ views on clinical readiness of Alzheimer's disease (AD) blood biomarkers (BBM) are not well understood. Method The Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment Biofluid‐Based Biomarkers Professional Interest Area conducted a survey to elicit clinician opinions on AD BBM implementation, including contexts of use, assay selection, reporting and result interpretation. Result Clinician respondents ( n = 212) practiced in Europe (56%), North America (24%), Caribbean and Central or South America (11%) and other continents (9%). Most respondents were medical doctors (80%) practicing in secondary or tertiary care (88%) (Table 1). For 56% of respondents, CSF AD biomarkers or amyloid PET were accessible, but 48% agreed and 52% disagreed with implementation of AD BBM in any clinical context (Table 2). Respondents emphasized the need for data from diverse populations and educational resources to support test interpretation. Conclusion Surveyed clinicians generally agreed with published appropriate use recommendations but were divided on AD BBM readiness for clinical use.

Abstract Background Plasma biomarkers offer improved accessibility compared to established diagnostic modalities for Alzheimer's disease (AD). However, current plasma biomarker candidates have not been tested comprehensively in diverse populations, and represent only a fraction of pathways dysregulated in AD. This study seeks to address these knowledge gaps through untargeted plasma biomarker discovery in African Americans (AA), an underrepresented group in AD research despite having a higher risk of developing dementia compared to non‐Hispanic Whites (NHW). Method Untargeted proteomics were generated using the SomaScan 7k platform on plasma samples from AA study participants clinically diagnosed as AD dementia ( n = 181) or cognitively unimpaired (CU; n = 142). Machine learning was implemented to identify a set of plasma proteins to distinguish between AD dementia and CU. Predictive models were constructed by elastic net regression optimized with nested cross‐validation on 6,853 proteins and evaluated by receiver operating characteristic analysis. The generalizability of plasma protein predictors was evaluated in an external Alzheimer's Disease Research Center cohort of 369 NHW and AA study participants diagnosed as AD dementia or CU. Result Thirty‐six proteins were selected as predictors. Proteins selected by machine learning were enriched for processes previously associated with AD including synaptic dysfunction, inflammation, vascular dysfunction, and amyloid processing. The 36 plasma proteins alone achieved an area under the curve (AUC) of 0.94 to classify AD dementia in the discovery cohort, representing a 22% improvement over a model including age and sex. The 36 proteins were validated in an external cohort also achieving AUC=0.94, a 35% improvement over age and sex. Conclusion Biomarker discovery combining untargeted proteomics and machine learning identified a set of plasma proteins that could serve as a novel and highly accurate diagnostic plasma biomarker panel for AD. Work is underway to assess the association of the predictive panel with p ‐tau217, medical comorbidities, and social/structural determinants of health.

Abstract Background Population aging is expected to increase the burden of Alzheimer's disease (AD) and related dementias (ADRD), yet up to 45% of ADRD risk may be modified. Disparities in ADRD burden among Hispanic/Latino and Black/African Americans suggests that socioeconomic deprivation and medical comorbidities may influence AD risk. The relationship between these factors, symptomatic age of onset, and neuropathologic findings in AD remains unclear. This study aims to investigate these associations in an ethnoracially diverse, neuropathologically diagnosed AD case series. Understanding these risk factors may help develop more effective AD risk reduction strategies. Method We queried the Florida Autopsied Multi‐Ethnic (FLAME) cohort database for neuropathologically‐diagnosed AD cases with available age onset of cognitive symptoms, who self‐identified as Hispanic/Latino ( n = 75) or Black/African American ( n = 22). Cases were matched to non‐Hispanic White decedents ( n = 101) by sex and year of birth. Medical records at the time of brain donation were retrospectively reviewed, assessing completeness for demographics, clinical characteristics, and medical comorbidities (Table 1). Result The table shows a comparative analysis stratified by young‐onset AD (YOAD, onset <65 years) and late‐onset AD (LOAD, onset ≥65 years). YOAD cases had a significantly higher level of education compared to LOAD ( p = 0.003). No differences were observed in sex , APOE ε4 carriership, or Area Deprivation Index between YOAD and LOAD. However, YOAD cases had a longer disease duration and were more likely to present with atypical clinical features compared to LOAD (both p <0.001). YOAD cases also showed a faster rate of decline on MMSE compared to LOAD ( p = 0.002), higher frequency of depression ( p = 0.018), and lower frequency of stroke/TIA ( p = 0.025). YOAD cases also had lower brain weight ( p = 0.041), less cerebrovascular disease burden ( p = 0.003) and higher neurofibrillary tangle density in association cortices ( p <0.001) compared to LOAD cases. Conclusion Socioeconomic deprivation and most medical comorbidities were not associated with AD symptomatic onset. Despite having higher education levels and lower cerebrovascular burden, YOAD decedents experienced faster cognitive decline but a longer disease course, with more frequent atypical clinical presentations and depression compared to LOAD. Further discussion will address the topographic distribution of tangle and plaque pathology.

Abstract BACKGROUND This systematic review and meta‐analysis aimed to assess the diagnostic test accuracy of blood‐based biomarkers (BBMs) for detecting Alzheimer's disease (AD) pathology in cognitively impaired individuals in specialized care settings. The overarching goal is to inform the development of a clinical practice guideline, led by the Alzheimer's Association, for use in clinical practice. METHODS A systematic search of MEDLINE, Embase, and Cochrane Library was conducted from January 2019 to November 2024. Studies evaluating the diagnostic test accuracy of plasma phosphorylated tau (p‐tau) and amyloid beta (Aβ) tests (p‐tau217, %p‐tau217, p‐tau181, p‐tau231, and Aβ42/Aβ40) compared to reference standard tests (cerebrospinal fluid [CSF] AD biomarkers, amyloid positron emission tomography [PET], or neuropathology) in individuals with cognitive impairment (mild cognitive impairment or dementia) in specialized care settings were included. Pooled diagnostic test accuracy measures were calculated, including sensitivity, specificity, and likelihood ratios. Across a range of pre‐test probabilities, we evaluated how much a positive or a negative test result would lead to a change in the probability of having amyloid positivity (post‐test probability). All analyses were conducted for each test within each biomarker. The Quality Assessment of Diagnostic Accuracy Studies 2 tool was used to evaluate the risk of bias, and the certainty of the evidence was assessed using the Grading of Recommendations, Assessment, Development, and Evaluation approach. RESULTS Across 49 observational studies meeting eligibility criteria, 31 different BBM tests were examined. When evaluated using a single cut‐point, the diagnostic test accuracy varied considerably across tests: the pooled sensitivity ranged from 49.3% (95% confidence interval [CI]: 41.2–57.4) to 91.4% (95% CI: 86.6–94.6), and the pooled specificity ranged from 61.5% (95% CI: 45.6–75.3) to 96.7% (95% CI: 87.8–99.2). Differences in post‐test probability based on a range of pre‐test probabilities varied greatly across tests. Furthermore, the certainty of evidence across tests ranged from moderate to very low. Most included studies were judged to be at high risk of bias, particularly in domains related to patient selection, index test conduct, and reference standard. CONCLUSION This systematic review provides a comprehensive synthesis of the current evidence on the diagnostic accuracy of BBMs for detecting AD pathology in cognitively impaired individuals in specialized care settings. The findings serve as a foundation for an accompanying clinical practice guideline that provides evidence‐based recommendations for BBM use in the clinical diagnostic pathway. Given continuous developments in this rapidly evolving field, ongoing evaluation will be critical to ensure the synthesized evidence and clinical guidelines remain up to date and maintain clinical relevance. Highlights This is the first comprehensive systematic review and meta‐analysis to evaluate the diagnostic accuracy of blood‐based biomarker (BBM) tests specifically in individuals with objective cognitive impairment seen in specialized care settings. Across 49 studies, BBM test performance varied widely. Pooled sensitivity ranged from 49.3% to 91.4% and specificity from 61.5% to 96.7%, depending on the analyte and assay platform. This review followed the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy, the Grading of Recommendations, Assessment, Development, and Evaluation approach to assess the certainty of evidence. This review served as the evidence base for the Alzheimer's Association's new clinical practice guidelines on BBMs, providing structured, evidence‐based guidance for implementing BBMs in the diagnostic workup of suspected Alzheimer's disease. The review underscores that BBM test performance is assay‐ and platform specific and advises clinicians and laboratory directors to interpret results in the context of the specific test used and integrate the results with a comprehensive clinical assessment.

Abstract INTRODUCTION We investigated associations of plasma proteins with blood‐based amyloid/tau/neurodegeneration/inflammation (A/T/N/I) biomarkers for Alzheimer's disease (AD). METHODS Plasma proteomics and clinical data from the Indiana AD Research Center ( N = 498) were used. Association analysis of plasma proteins with blood A/T/N/I biomarkers as well as diagnosis was performed, followed by replication in an independent cohort ( N = 323), network analysis, pathway enrichment, and machine learning classification to identify proteins and pathways related to AD risk. RESULTS We identified 35 proteins associated with AD, 20 of which were replicated in the independent cohort. We identified 150, 448, and 219 proteins associated with T/N/I biomarkers, respectively, revealing biomarker‐specific pathways. Network analysis identified two modules associated with T/N/I biomarkers, preserved in cerebrospinal fluid (CSF), and their enriched pathways. The classification model of proteins effectively differentiated AD (area under the curve [AUC] = 0.930). CONCLUSION Our findings suggest dysregulated plasma proteins and pathways in AD, enhancing our understanding of molecular mechanisms and diagnostic strategies for AD. Highlights Plasma proteins were identified as being associated with Alzheimer's disease (AD) and plasma biomarkers. The identified proteins were replicated in both plasma and cerebrospinal fluid (CSF) proteomics. The identified proteins were associated with AD biomarker‐specific pathways. The identified proteins improved the performance of the AD classification. Protein network analysis identified network modules and their enriched pathways.

Abstract INTRODUCTION African American (AA) individuals are underrepresented in biomarker studies for Alzheimer's disease (AD). Biomarkers that reflect the heterogeneity of AD and achieve the greatest accuracy across populations are sorely needed. METHODS Untargeted proteome measurements were obtained using the SomaScan 7k platform to identify novel plasma biomarkers for AD in AA participants with clinical diagnoses of AD dementia ( n = 181) and cognitively unimpaired (CU, n = 142). Machine learning was used to identify a set of plasma proteins that yielded the best classification accuracy. RESULTS A set of 36 proteins achieved an area under the curve (AUC) of 0.94 to classify AD dementia versus CU, a 16% improvement over age, sex, and apolipoprotein E ( APOE) . This finding was replicated in multiple plasma and brain datasets (AUCs 0.73–0.97). Our findings underscore the importance of matrisome and cerebrovascular dysfunction in AD pathophysiology. DISCUSSION This study demonstrates the potential of biomarker discovery through untargeted plasma proteomics and machine learning. Highlights Conducted large‐scale plasma proteomics in Alzheimer's disease (AD) versus cognitively unimpaired controls. Machine learning biomarker discovery was replicated in an independent cohort. Novel set of proteins distinguishes AD versus controls with high accuracy (area under the curve [AUC] = 0.94). Achieved reproducibility across multiple replication cohorts (AUC = 0.73–0.97). Network analyses implicates matrisome biology and cerebrovascular dysfunction.

Objective To determine the clinical features that identify patients with suspected rapidly progressive dementia (RPD) who will develop RPD. Methods Patients with suspected RPD were enrolled and followed at Mayo Clinic (Jacksonville, FL; January 2020 to October 2023) and Washington University (Saint Louis, MO; June 2016 to December 2019). Two dementia specialists independently reviewed clinical data and assigned diagnoses. Patients were diagnosed with RPD if they developed dementia within 1 year or incapacitation within 2 years of symptom onset. The associations between clinical features and causes and outcomes were explored via univariate and multivariate comparisons. Results Of 248 patients with suspected RPD, 185 (74.6%) met criteria for RPD. Patients with RPD were older (62.6 ± 14.5 vs 55.2 ± 18.1 years; p < 0.001), and more frequently diagnosed with Alzheimer's disease or related dementias (OR 3.13, 95% CI 1.40, 7.31) or Creutzfeldt–Jakob disease (OR 4.67, 95% CI 1.38, 15.75). Visual agnosia (15/183; 8.2%), substantial brain atrophy (27/182; 14.8%), and periodic epileptiform discharges (11/151; 7.3%) were exclusively detected in patients with RPD. After controlling for age, intensive care unit admission (OR 4.77, 95% CI 1.61, 14.12), lack of seizures (OR 2.47, 95% CI 1.15, 5.31), abnormal brain magnetic resonance imaging (OR 2.01, 95% CI 0.997, 4.05), and cerebrospinal fluid white blood cell count ≤5 cells/mm 3 (OR 3.30, 95% CI 1.60, 6.82) were also independently associated with RPD. Patients with RPD were more likely to die or develop severe dementia (mean time to outcome, 36.1 months; 95% CI 31.2–41.0). Interpretation Selected clinical features may identify patients with suspected RPD who are likely to continue to decline. Early recognition of these features may improve diagnostic accuracy and inform prognosis. ANN NEUROL 2025;98:764–776