Our staff editors continue to share exciting, interesting, and thought-provoking reading material in the recommended articles series.
This week, we would like to share several latest articles on Biomarker and Neurodegenerative Diseases.
Title: Increasing participant diversity in AD research: Plans for digital screening, blood testing, and a community-engaged approach in the Alzheimer's Disease Neuroimaging Initiative 4
Authors: Michael W Weiner, Dallas P Veitch, Melanie J Miller, Paul S Aisen, Bruce Albala, Laurel A Beckett, Robert C Green, Danielle Harvey, Clifford R Jack Jr, William Jagust, Susan M Landau, John C Morris, Rachel Nosheny, Ozioma C Okonkwo, Richard J Perrin, Ronald C Petersen, Monica Rivera-Mindt, Andrew J Saykin, Leslie M Shaw, Arthur W Toga, Duygu Tosun, John Q Trojanowski, Alzheimer's Disease Neuroimaging Initiative
Introduction: The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to validate biomarkers for Alzheimer's disease (AD) clinical trials. To improve generalizability, ADNI4 aims to enroll 50-60% of its new participants from underrepresented populations (URPs) using new biofluid and digital technologies. ADNI4 has received funding from the National Institute on Aging beginning September 2022.
Methods: ADNI4 will recruit URPs using community-engaged approaches. An online portal will screen 20,000 participants, 4000 of whom (50-60% URPs) will be tested for plasma biomarkers and APOE. From this, 500 new participants will undergo in-clinic assessment joining 500 ADNI3 rollover participants. Remaining participants (∼3500) will undergo longitudinal plasma and digital cognitive testing. ADNI4 will add MRI sequences and new PET tracers. Project 1 will optimize biomarkers in AD clinical trials.
Results and discussion: ADNI4 will improve generalizability of results, use remote digital and blood screening, and continue providing longitudinal clinical, biomarker, and autopsy data to investigators.
Keywords: Alzheimer's disease; amyloid; cerebrovascular disease; digital biomarkers; generalizability; mild cognitive impairment; plasma biomarkers; tau; underrepresented populations.
Access this article: https://doi.org/10.1002/alz.12797
Title：European consensus for the diagnosis of MCI and mild dementia: Preparatory phase
Authors: Cristina Festari, Federico Massa, Matteo Cotta Ramusino, Federica Gandolfo, Valentina Nicolosi, Stefania Orini, Dag Aarsland, Federica Agosta, Claudio Babiloni, Mercè Boada, Barbara Borroni, Stefano Cappa, Bruno Dubois, Kristian S Frederiksen, Lutz Froelich, Valentina Garibotto, Jean Georges, Alexander Haliassos, Oskar Hansson, Frank Jessen, Anita Kamondi, Roy P C Kessels, Silvia Morbelli, John T O'Brien, Markus Otto, Armand Perret-Liaudet, Francesca B Pizzini, Craig W Ritchie, Philip Scheltens, Mathieu Vandenbulcke, Ritva Vanninen, Frans Verhey, Meike W Vernooij, Tarek Yousry, Wiesje M Van Der Flier, Flavio Nobili, Giovanni B Frisoni
Type: Featured Article
Introduction: Etiological diagnosis of neurocognitive disorders of middle-old age relies on biomarkers, although evidence for their rational use is incomplete. A European task force is defining a diagnostic workflow where expert experience fills evidence gaps for biomarker validity and prioritization. We report methodology and preliminary results.
Methods: Using a Delphi consensus method supported by a systematic literature review, 22 delegates from 11 relevant scientific societies defined workflow assumptions.
Results: We extracted diagnostic accuracy figures from literature on the use of biomarkers in the diagnosis of main forms of neurocognitive disorders. Supported by this evidence, panelists defined clinical setting (specialist outpatient service), application stage (MCI-mild dementia), and detailed pre-assessment screening (clinical-neuropsychological evaluations, brain imaging, and blood tests).
Discussion: The Delphi consensus on these assumptions set the stage for the development of the first pan-European workflow for biomarkers' use in the etiological diagnosis of middle-old age neurocognitive disorders at MCI-mild dementia stages.
Highlights: Rational use of biomarkers in neurocognitive disorders lacks consensus in Europe. A consensus of experts will define a workflow for the rational use of biomarkers. The diagnostic workflow will be patient-centered and based on clinical presentation. The workflow will be updated as new evidence accrues.
Access this article: https://doi.org/10.1002/alz.12798
Title: Recent progress in the graphene-based biosensing approaches for the detection of Alzheimer's biomarkers
Authors: NadiyehRouhi, AbbasAkhgari, NedaOrouji, AlirezaNezami, MiladRahimzadegan, HosseinKamali
Alzheimer's disease (AD) is one of the most common types of neurodegenerative disorders. It is possible to identify AD early thanks to the measurement of specific biomarker levels. Owing to crucial roles of biomarkers in the AD, the detection of AD-related biomarkers may be suitable for predictive identification of AD. Biosensors is a novel tool that could be beneficial to appreciate recognition of several AD biomarkers as early as possible. Graphene and its derivatives containing graphene oxide (GO) and reduced-GO (rGO) can be good choice for biosensing approaches due to their unique properties. GO/rGO-based biosensors or nanosensors have been widely used for the determination of AD biomarkers. In this article, the general aspects of AD, its biomarkers, biosensors, and GO are overviewed. In addition, this review provides the current developments in the applications of graphene-based biosensors for recognition of AD biomarkers. Future perspectives and challenges of graphene-based biosensing as a new approach for detection of AD are discussed in brief as well.
Access this article: https://doi.org/10.1016/j.jpba.2022.115084
Title: Bioinformatics analysis of diagnostic biomarkers for Alzheimer's disease in peripheral blood based on sex differences and support vector machine algorithm
Authors: Wencan Ji, Ke An, Canjun Wang & Shaohua Wang
Type: Research Article
Background: The prevalence of Alzheimer's disease (AD) varies based on gender. Due to the lack of early stage biomarkers, most of them are diagnosed at the terminal stage. This study aimed to explore sex-specific signaling pathways and identify diagnostic biomarkers of AD.
Methods: Microarray dataset for blood was obtained from the Gene Expression Omnibus (GEO) database of GSE63060 to conduct differentially expressed genes (DEGs) analysis by R software limma. Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene set enrichment analysis (GSEA) were conducted. Immune checkpoint gene expression was compared between females and males. Using CytoHubba, we identified hub genes in a protein-protein interaction network (PPI). Then, we evaluated their distinct effectiveness using unsupervised hierarchical clustering. Support vector machine (SVM) and ten-fold cross-validation were used to further verify these biomarkers. Lastly, we confirmed our findings by using another independent dataset.
Results: A total of 37 female-specific DEGs and 27 male-specific DEGs were identified from GSE63060 datasets. Analyses of enrichment showed that female-specific DEGs primarily focused on energy metabolism, while male-specific DEGs mostly involved in immune regulation. Three immune-checkpoint-relevant genes dysregulated in males. In females, however, these eight genes were not differentially expressed. SNRPG, RPS27A, COX7A2, ATP5PO, LSM3, COX7C, PFDN5, HINT1, PSMA6, RPS3A and RPL31 were regarded as hub genes for females, while SNRPG, RPL31, COX7C, RPS27A, RPL35A, RPS3A, RPS20 and PFDN5 were regarded as hub genes for males. Thirteen hub genes mentioned above was significantly lower in both AD and mild cognitive impairment (MCI). The diagnostic model of 15-marker panel (13 hub genes with sex and age) was developed. Both the training dataset and the independent validation dataset have area under the curve (AUC) with a high value (0.919, 95%CI 0.901-0.929 and 0.803, 95%CI 0.789-0.826). Based on GSEA for hub genes, they were associated with some aspects of AD pathogenesis.
Conclusion: DEGs in males and females contribute differently to AD pathogenesis. Algorithms combining blood-based biomarkers may improve AD diagnostic accuracy, but large validation studies are needed.
Access this article: https://doi.org/10.1186/s41065-022-00252-x
Title：Chapter 16 - Classification of neurodegenerative disorders using machine learning techniques
Authors: Charles O. Adetunji,Olugbemi T. Olaniyan,Olorunsola Adeyomoye, Ayobami Dare,Mayowa J. Adeniyi,Alex Enoch
Book: Artificial Intelligence for Neurological Disorders
According to the World Health Organization, neurodegenerative diseases such as Huntington's disease, Parkinson's disease, progressive brain dysfunction, vascular dementia, cognitive impairment, Alzheimer's disease, and Amyotrophic Lateral Sclerosis account for more than seven million deaths and morbidity of one billion people worldwide, making research on a variety of neurodegenerative diseases particularly important. Several improvements in neuroimaging data acquisition approaches such as diffusion Magnetic Resonance and data mining from electroencephalogram are channeled toward classification of different neurodegenerative diseases and treatment options. Though, several challenges are encountered in the proper diagnosis, analysis, and classification of these neurodegenerative disorders such as increased dimensionality, nonlinearity, and nonstationarity. As a result, various machine learning approaches and algorithms such as reinforcement learning, semisupervised learning, unsupervised learning, supervised learning, deep learning, decision tree, BF tree, Bagging, Random Forest tree, RBF networks, and evolutionary learning are required to accurately classify and treat many of these neurodegenerative diseases. Several gait biomarkers, postural disorders, memory disorders, could be employed as quantitative assessment in early diagnosis and treatment strategies utilizing computer assisted diagnostic systems. Therefore, this chapter will focus on recent advances in the classification of neurodegenerative diseases utilizing machine learning algorithms.
Access this article: https://doi.org/10.1016/B978-0-323-90277-9.00028-6