Authors: Petersen, Melissa | Hall, James | Parsons, Thomas | Johnson, Leigh | O’Bryant, Sid
Article Type: Research Article
Abstract: Background: Recent work has supported use of blood-based biomarkers in detection of amnestic mild cognitive impairment (MCI). Inclusion of neuropsychological measures has shown promise in enhancing utility of biomarkers to detect disease. Objective: The present study sought to develop cognitive-biomarker profiles for detection of MCI. Methods: Data were analyzed on 463 participants (normal control n = 378; MCI n = 85) from HABLE. Random forest analyses determined proteomic profile of MCI. Separate linear regression analyses determined variance accounted for by select biomarkers per neuropsychological measure. When neuropsychological measure with the least shared variance was identified, it was then combined with select biomarkers …to create a biomarker-cognitive profile. Results: The biomarker-cognitive profile was 90% accurate in detecting MCI. Among amnestic MCI cases, the detection accuracy of the biomarker-cognitive profile was 92% and increased to 94% with demographic variables. Conclusion: The biomarker-cognitive profile for MCI was highly accurate in its detection with use of only five biomarkers. Show more
Keywords: Blood based, biomarkers, mexican american, mild cognitive impairment, neuropsychology
DOI: 10.3233/JAD-191264
Citation: Journal of Alzheimer's Disease, vol. 75, no. 3, pp. 739-750, 2020
Authors: Mozdbar, Sima | Petersen, Melissa | Zhang, Fan | Johnson, Leigh | Tolman, Alex | Nyalakonda, Ramyashree | Gutierrez, Alejandra | O’Bryant, Sid
Article Type: Research Article
Abstract: Background: Despite the diagnostic accuracy of advanced neurodiagnostic procedures, the detection of Alzheimer’s disease (AD) remains poor in primary care. There is an urgent need for screening tools to aid in the detection of early AD. Objective: This study examines the predictive ability of structural retinal biomarkers in detecting cognitive impairment in a primary care setting. Methods: Participants were recruited from Alzheimer’s Disease in Primary Care (ADPC) study. As part of the ADPC Retinal Biomarker Study (ADPC RBS), visual acuity, an ocular history questionnaire, eye pressure, optical coherence tomography (OCT) imaging, and fundus imaging was performed. Results: Data were examined …on n = 91 participants. The top biomarkers for predicting cognitive impairment included the inferior quadrant of the outer retinal layers, all four quadrants of the peripapillary retinal nerve fiber layer, and the inferior quadrant of the macular retinal nerve fiber layer. Conclusion: The current data provides strong support for continued investigation into structural retinal biomarkers, particularly the retinal nerve fiber layer, as screening tools for AD. Show more
Keywords: Alzheimer’s disease, cognitive impairment, optical coherence tomography, retinal biomarkers
DOI: 10.3233/ADR-220070
Citation: Journal of Alzheimer's Disease Reports, vol. 6, no. 1, pp. 749-755, 2022
Authors: Zhang, Fan | Petersen, Melissa | Johnson, Leigh | Hall, James | O’Bryant, Sid E.
Article Type: Research Article
Abstract: Background: There is a need for more reliable diagnostic tools for the early detection of Alzheimer’s disease (AD). This can be a challenge due to a number of factors and logistics making machine learning a viable option. Objective: In this paper, we present on a Support Vector Machine Leave-One-Out Recursive Feature Elimination and Cross Validation (SVM-RFE-LOO) algorithm for use in the early detection of AD and show how the SVM-RFE-LOO method can be used for both classification and prediction of AD. Methods: Data were analyzed on n = 300 participants (n = 150 AD; n = 150 cognitively normal controls). Serum samples were …assayed via a multi-plex biomarker assay platform using electrochemiluminescence (ECL). Results: The SVM-RFE-LOO method reduced the number of features in the model from 21 to 16 biomarkers and achieved an area under the curve (AUC) of 0.980 with a sensitivity of 94.0% and a specificity of 93.3%. When the classification and prediction performance of SVM-RFE-LOO was compared to that of SVM and SVM-RFE, we found similar performance across the models; however, the SVM-RFE-LOO method utilized fewer markers. Conclusion: We found that 1) the SVM-RFE-LOO is suitable for analyzing noisy high-throughput proteomic data, 2) it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features, and 3) it can improve the prediction performance. Our recursive feature elimination model can serve as a general model for biomarker discovery in other diseases. Show more
Keywords: Alzheimer’s disease, blood biomarkers, machine learning, recursive feature elimination, support vector machine
DOI: 10.3233/JAD-201254
Citation: Journal of Alzheimer's Disease, vol. 79, no. 4, pp. 1691-1700, 2021
Authors: O’Bryant, Sid E. | Zhang, Fan | Petersen, Melissa | Johnson, Leigh | Hall, James | Rissman, Robert A.
Article Type: Research Article
Abstract: Background: The REFLECT trials were conducted to examine the treatment of mild-to-moderate Alzheimer’s disease utilizing a peroxisome proliferator-activated receptor gamma agonist. Objective: To generate a predictive biomarker indicative of positive treatment response using samples from the previously conducted REFLECT trials. Methods: Data were analyzed on 360 participants spanning multiple negative REFLECT trials, which included treatment with rosiglitazone and rosiglitazone XR. Support vector machine analyses were conducted to generate a predictive biomarker profile. Results: A pre-defined 6-protein predictive biomarker (IL6, IL10, CRP, TNFα, FABP-3, and PPY) correctly classified treatment response with 100%accuracy across study arms for REFLECT Phase II trial (AVA100193) …and multiple Phase III trials (AVA105640, AV102672, and AVA102670). When the data was combined across all rosiglitazone trial arms, a global RSG-predictive biomarker with the same 6-protein predictive biomarker was able to accurately classify 98%of treatment responders. Conclusion: A predictive biomarker comprising of metabolic and inflammatory markers was highly accurate in identifying those patients most likely to experience positive treatment response across the REFLECT trials. This study provides additional proof-of-concept that a predictive biomarker can be utilized to help with screening and predicting treatment response, which holds tremendous benefit for clinical trials. Show more
Keywords: Alzheimer’s disease, clinical trial, predictive biomarker, rosiglitazone
DOI: 10.3233/JAD-201610
Citation: Journal of Alzheimer's Disease, vol. 81, no. 2, pp. 557-568, 2021
Authors: O’Bryant, Sid E. | Petersen, Melissa | Hall, James | Johnson, Leigh | for the HABS-HD Study Team
Article Type: Research Article
Abstract: Background: Despite the tremendous amount of research on Alzheimer’s disease (AD) biomarkers, very little data is available regarding the fundamental biomarkers of AD among Mexican Americans. Objective: Here we sought to examine the link between metabolic markers and brain amyloid among Mexican Americans as compared to non-Hispanic whites from the Health & Aging Brain Study –Health Disparities (HABS-HD) cohort. Methods: PET amyloid (florbetaben) data was analyzed from 34 Mexican American and 22 non-Hispanic white participants. Results: Glucagon (t = 3.84, p < 0.001) and insulin (t = –2.56, p = 0.02) were both significantly related to global SUVR levels among Mexican Americans. Glucagon and …insulin were both related to most ROIs. No metabolic markers were significantly related to brain amyloid levels among non-Hispanic whites. Conclusion: Metabolic markers are related to brain amyloid burden among Mexican Americans. Given the increased risk for diabetes, additional research is needed to determine the impact of diabetes on core AD biomarkers among this underserved population. Show more
Keywords: Alzheimer’s disease, amyloid, health disparities, Hispanic
DOI: 10.3233/JAD-215620
Citation: Journal of Alzheimer's Disease, vol. 86, no. 4, pp. 1745-1750, 2022
Authors: Zhang, Fan | Petersen, Melissa | Johnson, Leigh | Hall, James | O’Bryant, Sid E.
Article Type: Research Article
Abstract: Background: Blood biomarkers have the potential to transform Alzheimer’s disease (AD) diagnosis and monitoring, yet their integration with common medical comorbidities remains insufficiently explored. Objective: This study aims to enhance blood biomarkers’ sensitivity, specificity, and predictive performance by incorporating comorbidities. We assess this integration’s efficacy in diagnostic classification using machine learning, hypothesizing that it can identify a confident set of predictive features. Methods: We analyzed data from 1,705 participants in the Health and Aging Brain Study-Health Disparities, including 116 AD patients, 261 with mild cognitive impairment, and 1,328 cognitively normal controls. Blood samples were assayed using electrochemiluminescence and single molecule …array technology, alongside comorbidity data gathered through clinical interviews and medical records. We visually explored blood biomarker and comorbidity characteristics, developed a Feature Importance and SVM-based Leave-One-Out Recursive Feature Elimination (FI-SVM-RFE-LOO) method to optimize feature selection, and compared four models: Biomarker Only, Comorbidity Only, Biomarker and Comorbidity, and Feature-Selected Biomarker and Comorbidity. Results: The combination model incorporating 17 blood biomarkers and 12 comorbidity variables outperformed single-modal models, with NPV12 at 92.78%, AUC at 67.59%, and Sensitivity at 65.70%. Feature selection led to 22 chosen features, resulting in the highest performance, with NPV12 at 93.76%, AUC at 69.22%, and Sensitivity at 70.69%. Additionally, interpretative machine learning highlighted factors contributing to improved prediction performance. Conclusions: In conclusion, combining feature-selected biomarkers and comorbidities enhances prediction performance, while feature selection optimizes their integration. These findings hold promise for understanding AD pathophysiology and advancing preventive treatments. Show more
Keywords: Alzheimer’s disease, blood biomarkers, comorbidities, machine learning, recursive feature elimination, support vector machine
DOI: 10.3233/JAD-230755
Citation: Journal of Alzheimer's Disease, vol. 96, no. 4, pp. 1529-1546, 2023
Authors: O’Bryant, Sid E. | Zhang, Fan | Johnson, Leigh A. | Hall, James | Petersen, Melissa | Oh, Esther S. | Lyketsos, Constantine G. | Rissman, Robert A.
Article Type: Research Article
Abstract: Background: The Alzheimer’s Disease Anti-inflammatory Prevention Trial (ADAPT) was the first-ever large-scale anti-inflammatory prevention trial targeting Alzheimer’s disease. Objective: The overall goal of this study was to evaluate predictive blood biomarker profiles that identified individuals most likely to be responders on NSAID treatment or placebo at 12 and 24 months. Methods: Baseline (n = 193) and 12-month (n = 562) plasma samples were assayed. The predictive biomarker profile was generated using SVM analyses with response on treatment (yes/no) as the outcome variable. Results: Baseline (AUC = 0.99) and 12-month (AUC = 0.99) predictive biomarker profiles were highly accurate in predicting response on Celecoxib arm at 12 …and 24 months. The baseline (AUC = 0.95) and 12-month (AUC = 0.9) predictive biomarker profile predicting response on Naproxen were also highly accurate at 12 and 24 months. The baseline (AUC = 0.93) and 12-month (AUC = 0.99) predictive biomarker profile was also highly accurate in predicting response on placebo. As with our prior work, the profiles varied by treatment arm. Conclusions: The current results provide additional support for a precision medicine model for treating and preventing Alzheimer’s disease. Show more
Keywords: Alzheimer’s disease, bioinformatics, biomarkers, clinical trial, inflammation, precision medicine, prevention, proteomics
DOI: 10.3233/JAD-230317
Citation: Journal of Alzheimer's Disease, vol. 95, no. 4, pp. 1609-1622, 2023
Authors: O’Bryant, Sid E. | Petersen, Melissa | Hall, James | Johnson, Leigh A. | Barber, Robert | Phillips, Nicole | Braskie, Meredith N. | Yaffe, Kristine | Rissman, Robert | Toga, Arthur
Article Type: Research Article
Abstract: Background: Despite tremendous advancements in the field, our understanding of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) among Mexican Americans remains limited. Objective: The aim of this study was to characterize MCI and dementia among Mexican Americans and non-Hispanic whites. Methods: Baseline data were analyzed from n = 1,705 (n = 890 Mexican American; n = 815 non-Hispanic white) participants enrolled in the Health and Aging Brain Study-Health Disparities (HABS-HD). Results: Among Mexican Americans, age (OR = 1.07), depression (OR = 1.09), and MRI-based neurodegeneration (OR = 0.01) were associated with dementia, but none of these factors were associated with MCI. Among non-Hispanic whites, male gender (OR = 0.33), neighborhood …deprivation (OR = 1.34), depression (OR = 1.09), and MRI-based neurodegeneration (OR = 0.03) were associated with MCI, while depression (OR = 1.09) and APOE ɛ 4 genotype (OR = 4.38) were associated with dementia. Conclusion: Findings from this study revealed that the demographic, clinical, sociocultural and biomarker characteristics of MCI and dementia are different among Mexican Americans as compared to non-Hispanic whites. Show more
Keywords: Alzheimer’s disease, health disparities, Hispanic, mild cognitive impairment
DOI: 10.3233/JAD-220300
Citation: Journal of Alzheimer's Disease, vol. 90, no. 2, pp. 905-915, 2022
Authors: O’Bryant, Sid E. | Zhang, Fan | Petersen, Melissa | Hall, James R. | Johnson, Leigh A. | Yaffe, Kristine | Braskie, Meredith | Vig, Rocky | Toga, Arthur W. | Rissman, Robert A. | for the HABS-HD Study Team
Article Type: Research Article
Abstract: Background: Hispanics are expected to experience the largest increase in Alzheimer’s disease (AD) and AD related dementias over the next several decades. However, few studies have examined biomarkers of AD among Mexican Americans, the largest segment of the U.S. Hispanic population. Objective: We sought to examine proteomic profiles of an MRI-based marker of neurodegeneration from the AT(N) framework among a multi-ethnic, community-dwelling cohort. Methods: Community-dwelling Mexican Americans and non-Hispanic white adults and elders were recruited. All participants underwent comprehensive assessments including an interview, functional exam, clinical labs, informant interview, neuropsychological testing, and 3T MRI of the brain. A neurodegeneration MRI …meta-ROI biomarker for the AT(N) framework was calculated. Results: Data was examined from n = 1,291 participants. Proteomic profiles were highly accurate for detecting neurodegeneration (i.e., N +) among both Mexican Americans (AUC = 1.0) and non-Hispanic whites (AUC = 0.98). The proteomic profile of N + was different between ethnic groups. Further analyses revealed that the proteomic profiles of N + varied by diagnostic status (control, MCI, dementia) and ethnicity (Mexican American versus non-Hispanic whites) though diagnostic accuracy was high for all classifications. Conclusion: A proteomic profile of neurodegeneration has tremendous value and point towards novel diagnostic and intervention opportunities. The current findings demonstrate that the underlying biological factors associated with neurodegeneration are different between Mexican Americans versus non-Hispanic whites as well as at different levels of disease progression. Show more
Keywords: Alzheimer’s disease, biomarkers, diversity, hispanic, mexican american, mild cognitive impairment, neurodegeneration
DOI: 10.3233/JAD-210543
Citation: Journal of Alzheimer's Disease, vol. 86, no. 3, pp. 1243-1254, 2022
Authors: Petersen, Melissa E. | Rafii, Michael S. | Zhang, Fan | Hall, James | Julovich, David | Ances, Beau M. | Schupf, Nicole | Krinsky-McHale, Sharon J. | Mapstone, Mark | Silverman, Wayne | Lott, Ira | Klunk, William | Head, Elizabeth | Christian, Brad | Foroud, Tatiana | Lai, Florence | Diana Rosas, H. | Zaman, Shahid | Wang, Mei-Cheng | Tycko, Benjamin | Lee, Joseph H. | Handen, Benjamin | Hartley, Sigan | Fortea, Juan | O’Bryant, Sid | for the Alzheimer’s Biomarker Consortium –Down Syndrome (ABC-DS)
Article Type: Research Article
Abstract: Background: The need for diagnostic biomarkers of cognitive decline is particularly important among aging adults with Down syndrome (DS). Growing empirical support has identified the utility of plasma derived biomarkers among neurotypical adults with mild cognitive impairment (MCI) and Alzheimer’s disease (AD); however, the application of such biomarkers has been limited among the DS population. Objective: This study aimed to investigate the cross-sectional diagnostic performance of plasma neurofilament light chain (Nf-L) and total-tau, individually and in combination among a cohort of DS adults. Methods: Plasma samples were analyzed from n = 305 (n = 225 cognitively stable (CS); n = 44 MCI-DS; n … = 36 DS–AD) participants enrolled in the Alzheimer’s Biomarker Consortium —Down Syndrome. Results: In distinguishing DS-AD participants from CS, Nf-L alone produced an AUC of 90%, total-tau alone reached 74%, and combined reached an AUC of 86%. When age and gender were included, AUC increased to 93%. Higher values of Nf-L, total-tau, and age were all shown to be associated with increased risk for DS-AD. When distinguishing MCI-DS participants from CS, Nf-L alone produced an AUC of 65%, while total-tau alone reached 56%. A combined model with Nf-L, total-tau, age, and gender produced an AUC of 87%. Both higher values in age and total-tau were found to increase risk for MCI-DS; Nf-L levels were not associated with increased risk for MCI-DS. Conclusion: Advanced assay techniques make total-tau and particularly Nf-L useful biomarkers of both AD pathology and clinical status in DS and have the potential to serve as outcome measures in clinical trials for future disease-modifying drugs. Show more
Keywords: Neurofilament light chain, proteomics, sensitivity, specificity, total-tau, trisomy 21
DOI: 10.3233/JAD-201167
Citation: Journal of Alzheimer's Disease, vol. 79, no. 2, pp. 671-681, 2021