Novel Biomarkers in Alzheimer’s Disease Printed Edition of the Special Issue Published in Journal of Personalized Medicine www.mdpi.com/journal/jpm Chiara Villa Edited by Novel Biomarkers in Alzheimer’s Disease Novel Biomarkers in Alzheimer’s Disease Editor Chiara Villa MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin Editor Chiara Villa University of Milano-Bicocca Italy Editorial Office MDPI St. Alban-Anlage 66 4052 Basel, Switzerland This is a reprint of articles from the Special Issue published online in the open access journal Journal of Personalized Medicine (ISSN 2075-4426) (available at: https://www.mdpi.com/journal/ jpm/special issues/Biomarkers Alzheimer). For citation purposes, cite each article independently as indicated on the article page online and as indicated below: LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. Journal Name Year , Volume Number , Page Range. ISBN 978-3-03943-903-4 (Hbk) ISBN 978-3-03943-904-1 (PDF) c © 2020 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND. Contents About the Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix Chiara Villa Biomarkers for Alzheimer’s Disease: Where Do We Stand and Where Are We Going? Reprinted from: J. Pers. Med. 2020 , 10 , 238, doi:10.3390/jpm10040238 . . . . . . . . . . . . . . . . 1 Syed Haris Omar and John Preddy Advantages and Pitfalls in Fluid Biomarkers for Diagnosis of Alzheimer’s Disease Reprinted from: J. Pers. Med. 2020 , 10 , 63, doi:10.3390/jpm10030063 . . . . . . . . . . . . . . . . . 5 Kun Zou, Mohammad Abdullah and Makoto Michikawa Current Biomarkers for Alzheimer’s Disease: From CSF to Blood Reprinted from: J. Pers. Med. 2020 , 10 , 85, doi:10.3390/jpm10030085 . . . . . . . . . . . . . . . . . 25 Eva Aus ́ o, Violeta G ́ omez-Vicente and Gema Esquiva Biomarkers for Alzheimer’s Disease Early Diagnosis Reprinted from: J. Pers. Med. 2020 , 10 , 114, doi:10.3390/jpm10030114 . . . . . . . . . . . . . . . . 41 Cristina d’Abramo, Luciano D’Adamio and Luca Giliberto Significance of Blood and Cerebrospinal Fluid Biomarkers for Alzheimer’s Disease: Sensitivity, Specificity and Potential for Clinical Use Reprinted from: J. Pers. Med. 2020 , 10 , 116, doi:10.3390/jpm10030116 . . . . . . . . . . . . . . . . 67 Eleonora Del Prete, Maria Francesca Beatino, Nicole Campese, Linda Giampietri, Gabriele Siciliano, Roberto Ceravolo and Filippo Baldacci Fluid Candidate Biomarkers for Alzheimer’s Disease: A Precision Medicine Approach Reprinted from: J. Pers. Med. 2020 , 10 , 221, doi:10.3390/jpm10040221 . . . . . . . . . . . . . . . . 107 Marco Canevelli, Giulia Remoli, Ilaria Bacigalupo, Martina Valletta, Marco Toccaceli Blasi, Francesco Sciancalepore, Giuseppe Bruno, Matteo Cesari and Nicola Vanacore Use of Biomarkers in Ongoing Research Protocols on Alzheimer’s Disease Reprinted from: J. Pers. Med. 2020 , 10 , 68, doi:10.3390/jpm10030068 . . . . . . . . . . . . . . . . . 141 Efthalia Angelopoulou, Yam Nath Paudel, Mohd. Farooq Shaikh and Christina Piperi Flotillin: A Promising Biomarker for Alzheimer’s Disease Reprinted from: J. Pers. Med. 2020 , 10 , 20, doi:10.3390/jpm10020020 . . . . . . . . . . . . . . . . . 153 Chiara Villa, Marialuisa Lavitrano, Elena Salvatore and Romina Combi Molecular and Imaging Biomarkers in Alzheimer’s Disease: A Focus on Recent Insights Reprinted from: J. Pers. Med. 2020 , 10 , 61, doi:10.3390/jpm10030061 . . . . . . . . . . . . . . . . . 167 Walter J. Lukiw, Andrea Vergallo, Simone Lista, Harald Hampel and Yuhai Zhao Biomarkers for Alzheimer’s Disease (AD) and the Application of Precision Medicine Reprinted from: J. Pers. Med. 2020 , 10 , 138, doi:10.3390/jpm10030138 . . . . . . . . . . . . . . . . 197 Valeria D’Argenio and Daniela Sarnataro New Insights into the Molecular Bases of Familial Alzheimer’s Disease Reprinted from: J. Pers. Med. 2020 , 10 , 26, doi:10.3390/jpm10020026 . . . . . . . . . . . . . . . . . 209 v Valentina Bessi, Juri Balestrini, Silvia Bagnoli, Salvatore Mazzeo, Giulia Giacomucci, Sonia Padiglioni, Irene Piaceri, Marco Carraro, Camilla Ferrari, Laura Bracco, Sandro Sorbi and Benedetta Nacmias Influence of ApoE Genotype and Clock T3111C Interaction with Cardiovascular Risk Factors on the Progression to Alzheimer’s Disease in Subjective Cognitive Decline and Mild Cognitive Impairment Patients Reprinted from: J. Pers. Med. 2020 , 10 , 45, doi:10.3390/jpm10020045 . . . . . . . . . . . . . . . . . 223 Catia Scassellati, Miriam Ciani, Carlo Maj, Cristina Geroldi, Orazio Zanetti, Massimo Gennarelli and Cristian Bonvicini Behavioral and Psychological Symptoms of Dementia (BPSD): Clinical Characterization and Genetic Correlates in an Italian Alzheimer’s Disease Cohort Reprinted from: J. Pers. Med. 2020 , 10 , 90, doi:10.3390/jpm10030090 . . . . . . . . . . . . . . . . . 237 Md Shahjalal Hossain Khan and Vijay Hegde Obesity and Diabetes Mediated Chronic Inflammation: A Potential Biomarker in Alzheimer’s Disease Reprinted from: J. Pers. Med. 2020 , 10 , 42, doi:10.3390/jpm10020042 . . . . . . . . . . . . . . . . . 253 Chiara Argentati, Ilaria Tortorella, Martina Bazzucchi, Carla Emiliani, Francesco Morena and Sabata Martino The Other Side of Alzheimer’s Disease: Influence of Metabolic Disorder Features for Novel Diagnostic Biomarkers Reprinted from: J. Pers. Med. 2020 , 10 , 115, doi:10.3390/jpm10030115 . . . . . . . . . . . . . . . . 273 Simon M. Bell, Matteo De Marco, Katy Barnes, Pamela J. Shaw, Laura Ferraiuolo, Daniel J. Blackburn, Heather Mortiboys and Annalena Venneri Deficits in Mitochondrial Spare Respiratory Capacity Contribute to the Neuropsychological Changes of Alzheimer’s Disease Reprinted from: J. Pers. Med. 2020 , 10 , 32, doi:10.3390/jpm10020032 . . . . . . . . . . . . . . . . . 309 Agostino Chiaravalloti, Maria Ricci, Daniele Di Biagio, Luca Filippi, Alessandro Martorana and Orazio Schillaci The Brain Metabolic Correlates of the Main Indices of Neuropsychological Assessment in Alzheimer’s Disease † Reprinted from: J. Pers. Med. 2020 , 10 , 25, doi:10.3390/jpm10020025 . . . . . . . . . . . . . . . . . 325 Anna Picca, Daniela Ronconi, H ́ elio J. Coelho-Junior, Riccardo Calvani, Federico Marini, Alessandra Biancolillo, Jacopo Gervasoni, Aniello Primiano, Cristina Pais, Eleonora Meloni, Domenico Fusco, Maria Rita Lo Monaco, Roberto Bernabei, Maria Camilla Cipriani, Emanuele Marzetti and Rosa Liperoti The “develOpment of metabolic and functional markers of Dementia IN Older people” (ODINO) Study: Rationale, Design and Methods Reprinted from: J. Pers. Med. 2020 , 10 , 22, doi:10.3390/jpm10020022 . . . . . . . . . . . . . . . . . 335 Rebecca Power, John M. Nolan, Alfonso Prado-Cabrero, Robert Coen, Warren Roche, Tommy Power, Alan N. Howard and R ́ ıona Mulcahy Targeted Nutritional Intervention for Patients with Mild Cognitive Impairment: The Cognitive impAiRmEnt Study (CARES) Trial 1 Reprinted from: J. Pers. Med. 2020 , 10 , 43, doi:10.3390/jpm10020043 . . . . . . . . . . . . . . . . . 349 vi Valeria Guglielmi, Davide Quaranta, Ilaria Mega, Emanuele Maria Costantini, Claudia Carrarini, Alice Innocenti and Camillo Marra Semantic Priming in Mild Cognitive Impairment and Healthy Subjects: Effect of Different Time of Presentation of Word-Pairs Reprinted from: J. Pers. Med. 2020 , 10 , 57, doi:10.3390/jpm10030057 . . . . . . . . . . . . . . . . . 379 Paolo Maria Rossini, Francesca Miraglia, Francesca Al ` u, Maria Cotelli, Florinda Ferreri, Riccardo Di Iorio, Francesco Iodice and Fabrizio Vecchio Neurophysiological Hallmarks of Neurodegenerative Cognitive Decline: The Study of Brain Connectivity as a Biomarker of Early Dementia Reprinted from: J. Pers. Med. 2020 , 10 , 34, doi:10.3390/jpm10020034 . . . . . . . . . . . . . . . . . 391 Sean X Naughton, Urdhva Raval and Giulio M. Pasinetti The Viral Hypothesis in Alzheimer’s Disease: Novel Insights and Pathogen-Based Biomarkers Reprinted from: J. Pers. Med. 2020 , 10 , 74, doi:10.3390/jpm10030074 . . . . . . . . . . . . . . . . . 419 vii About the Editor Chiara Villa is currently an Assistant Professor in Pathology at the University of Milano-Bicocca. She started her research activity in 2006 in Department of Neurological Sciences, University of Milan, Fondazione C` a Granda, IRCCS, Ospedale Maggiore Policlinico, where she obtained her Ph.D. in Molecular Medicine in 2009. Her research activity has mainly focused on the study of novel biomarkers (e.g., microRNAs, pro-inflammatory cytokines) and genetic risk factors in key genes for the early detection and/or progression of two neurodegenerative diseases: Alzheimer’s disease and frontotemporal lobar degeneration. In 2014, she moved to the University of Milano-Bicocca and joined a research group that studies the molecular bases of autism spectrum disorders and sleep disorders, including restless legs syndrome and nocturnal frontal lobe epilepsy. Her work is now carried out by applying her previous research experience in this field through a molecular approach. At the moment, she is co-author of 58 research articles published in peer-reviewed journals with a personal H-index of 20. ix Journal of Personalized Medicine Editorial Biomarkers for Alzheimer’s Disease: Where Do We Stand and Where Are We Going? Chiara Villa School of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy; chiara.villa@unimib.it Received: 5 November 2020; Accepted: 17 November 2020; Published: 20 November 2020 Alzheimer’s disease (AD) is an age-related neurodegenerative and progressive disorder representing the most common form of dementia in older adults. AD is clinically characterized by significant cognitive impairments, behavioral changes, sleep disorders, and loss of functional autonomy until the patient becomes completely dependent on the care of family members and healthcare workers [ 1 ]. As the population ages worldwide, the number of people su ff ering from AD is growing rapidly, making this disorder a major public health issue. Actually, the leading biomarkers in clinical practice are directed at the early identification of the two neuropathological hallmarks of AD, namely, amyloid- β (A β ) plaques and neurofibrillary tangles (NFTs), constituted by hyper-phosphorylated paired helical filaments of the microtubule-associated protein tau. The diagnostic criteria rely on the measures of A β , phosphorylated (p-tau), and total tau (t-tau) protein levels in the cerebrospinal fluid (CSF) of patients aided by advanced neuroimaging methods such as magnetic resonance imaging (MRI) and positron emission tomography (PET) [ 2 ]. However, the pathological changes silently accumulate in the brain over years or even decades before the onset of symptoms. Therefore, the current challenge is the searching for novel biomarkers to optimize the early diagnosis of AD in the pre-symptomatic stages, essential to start treatments and to propose personalized therapeutic solutions to individual patients. This Special Issue gathers six original research articles, thirteen literature reviews, one commentary, and one protocol on recent e ff orts toward the discovery of novel biomarker candidates exploited in di ff erent research areas, including biological fluids, genetic / epigenetic factors, pathogens, inflammation, metabolism, nutrition, obesity, or neuropsychological changes (Figure 1). It is not surprising that the most of papers are addressed to review the current knowledge about biomarkers detected in di ff erent biological fluids, which are mainly related to pathophysiological processes occurring in AD (e.g., vascular dysfunction, neuroinflammation, and synaptic and neuronal integrity). These reviews largely describe and discuss potential biomarkers detected in CSF or blood as well as in alternative non-invasive body fluids and their possible use in early diagnosis [ 3 – 7 ] or ongoing research protocols on AD [ 8 ]. Among them, an emerging role of flotillin as promising biomarker for AD has been proposed by some authors [ 9 ]. Moreover, to partially overcome the limitations of biological fluids, advanced brain imaging techniques provide an attractive alternative for the identification of AD-related structural and functional biomarkers [10]. Integrated datasets of multi-faceted AD biomarkers and data-driven analytical methodologies may be involved in the application of the “precision medicine”, aimed to unravel many aspects of AD heterogeneity and to expand the current treatment strategies to help guide more e ff ective diagnosis and clinical management of the disorder [11]. J. Pers. Med. 2020 , 10 , 238; doi:10.3390 / jpm10040238 www.mdpi.com / journal / jpm 1 J. Pers. Med. 2020 , 10 , 238 Figure 1. An overview of di ff erent research field for exploring potential biomarkers for Alzheimer’s disease. This figure was created with the support of BioRender.com. Given the central role of genetics in the development of AD, some authors reviewed emerging candidate genes for familial AD, as well as inherited risk factors, in order to improve the prognostic identification and management of the at-risk individuals. A better knowledge of these genes and their correlated molecular defects will further provide potential targets for the treatment of the disease [ 12 ]. One study has reported original results on the association between AD-related polymorphisms and cardiovascular risk factors, which influence the progression to the disorder. Therefore, understanding the molecular mechanisms of this interaction could allow the development of new personalized therapeutic approaches for treating AD [ 13 ]. Focusing on the occurrence of behavioral and psychological symptoms of dementia in AD (BPSD), other authors have found an interesting association between APOE and MTHFR genetic variants and BPSD, expanding the knowledge about the BPSD etiopathogenetic mechanisms, which in turn, leads not only improve the clinical / diagnostic assessment, but also to better definite suitable treatments [14]. As an early event in the pathogenesis of AD, some authors speculated that chronic inflammation should be considered as a potential biomarker in the treatment strategies for AD. Interestingly, inflammation is emerging as the central mechanistic link among diabetes, obesity, and cognitive decline in patients a ff ected by AD. These authors discuss how diabetes and obesity could lead to both systemic and neuro-inflammation, hypothesizing an association with impaired mitochondrial health [ 15 ]. Indeed, AD has also been suggested as a metabolic disorder, owing to the fact that some genetic risk factors are key mediators in di ff erent metabolic pathways, including glucose, lipid, and energetic metabolism [ 16 ]. In this regard, Bell and collaborators demonstrate the strong correlation between fibroblast mitochondrial abnormalities and neuropsychological markers, suggesting the use of fibroblast metabolic assessment as an emergent biomarker of AD [ 17 ]. Similarly, another study reports that brain metabolism evaluated by 18F fluorodeoxyglucose (18F-FDG) uptake is moderately related to various neuropsychological tests [ 18 ]. Moreover, some authors conceived the “development of metabolic and functional markers of dementia in older people” (ODINO) protocol as an innovative multi-dimensional investigation in which clinical, functional, neuropsychological, and biological parameters are coupled 2 J. Pers. Med. 2020 , 10 , 238 with advanced statistical analyses in order to better identify possible biomarkers that can predict the conversion from mild cognitive impairment (MCI), the prodromal stage of dementia, to AD [19]. Among individuals with MCI, two additional papers reported original results. The randomized cognitive impairment study (CARES) clinical trial demonstrated that targeted nutritional intervention with ω -3FAs, carotenoids, and vitamin E significantly improves the cognitive performances [ 20 ]. Other authors showed that an experimental assessment of semantic priming in MCI seems to represent a good paradigm to evaluate subclinical impairment of the semantic system in the early stages of the AD pathology [ 21 ]. Finally, an outstanding review discussed how neurophysiological techniques, evaluating mechanisms of synaptic function and brain connectivity, may represent valid biomarkers for screening MCI individuals by the application of artificial intelligence (i.e., learning machine) [22]. Based on studies linking di ff erent pathogens with AD and age-related cognitive decline, Naughton and collaborators discuss an interesting role of pathogen-associated biomarkers as a novel tool for evaluating and decreasing AD risk across the population [23]. In conclusion, all articles appearing in this Special Issue cover attractive and current topics of a wide range of biomarkers in the basic research, clinical diagnosis, prognosis, and therapeutic strategies of AD, the most common form of neurodegenerative disorder and a major health challenge with significant social and economic consequences. Early diagnosis entailing the ability to detect AD in asymptomatic patients still remains a big challenge. Therefore, implementing a combination of the aforementioned biomarkers into a diagnostic setting may likely allow the identification of at-risk patients during pre-symptomatic stages necessary to start treatments and to suggest personalized therapeutic strategies. Funding: The research received no external funding. Acknowledgments: I am very grateful to all authors who have provided excellent contributions to this Special Issue. Moreover, I would like to thank Journal of Personalized Medicine for o ff ering me the opportunity to make this Special Issue a reality, and in particular, Crystal Feng for her availability, professionalism, help, constant support, and kindness. Finally, I would like to acknowledge the excellent and e ffi cient work of the expert reviewers who reviewed submissions in a timely, fair, and constructive manner. Conflicts of Interest: The author declares no conflict of interest. References 1. Villa, C.; Ferini-Strambi, L.; Combi, R. The Synergistic Relationship between Alzheimer’s Disease and Sleep Disorders: An Update. J. Alzheimers Dis. 2015 , 46 , 571–580. [CrossRef] [PubMed] 2. Jack, C.R., Jr.; Bennett, D.A.; Blennow, K.; Carrillo, M.C.; Dunn, B.; Haeberlein, S.B.; Holtzman, D.M.; Jagust, W.; Jessen, F.; Karlawish, J.; et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 2018 , 14 , 535–562. [CrossRef] [PubMed] 3. Omar, S.H.; Preddy, J. Advantages and Pitfalls in Fluid Biomarkers for Diagnosis of Alzheimer’s Disease. J. Pers. Med. 2020 , 10 , 63. [CrossRef] 4. Zou, K.; Abdullah, M.; Michikawa, M. Current biomarkers for Alzheimer’s disease: From CSF to blood. J. Pers. Med. 2020 , 10 , 85. [CrossRef] 5. Aus ó , E.; G ó mez-Vicente, V.; Esquiva, G. Biomarkers for Alzheimer’s disease early diagnosis. J. Pers. Med. 2020 , 10 , 114. [CrossRef] 6. D’Abramo, C.; D’Adamio, L.; Giliberto, L. Significance of blood and CSF biomarkers for Alzheimer’s disease: Use and specificity. J. Pers. Med. 2020 , 10 , 116. [CrossRef] 7. Del Prete, E.; Beatino, M.F.; Campese, N.; Giampietri, L.; Siciliano, G.; Ceravolo, R.; Baldacci, F. Fluid candidate biomarkers for Alzheimer’s Disease: A precision medicine approach. J. Pers. Med. 2020 , 10 , 221. [CrossRef] 8. Canevelli, M.; Remoli, G.; Bacigalupo, I.; Valletta, M.; Toccaceli Blasi, M.; Sciancalepore, F.; Bruno, G.; Cesari, M.; Vanacore, N. Use of Biomarkers in Ongoing Research Protocols on Alzheimer’s Disease. J. Pers. Med. 2020 , 10 , 68. [CrossRef] 9. Angelopoulou, E.; Paudel, Y.N.; Shaikh, M.F.; Piperi, C. Flotillin: A Promising Biomarker for Alzheimer’s Disease. J. Pers. Med. 2020 , 10 , 20. [CrossRef] 3 J. Pers. Med. 2020 , 10 , 238 10. Villa, C.; Lavitrano, M.; Salvatore, E.; Combi, R. Molecular and Imaging Biomarkers in Alzheimer’s Disease: A Focus on Recent Insights. J. Pers. Med. 2020 , 10 , 61. [CrossRef] 11. Lukiw, W.J.; Vergallo, A.; Lista, S.; Hampel, H.; Zhao, Y. Biomarkers for Alzheimer’s disease (AD) and the application of Precision Medicine. J. Pers. Med. 2020 , 10 , 138. [CrossRef] [PubMed] 12. D’Argenio, V.; Sartanaro, D. New insights into the molecular bases of familial Alzheimer’s disease. J. Pers. Med. 2020 , 10 , 26. [CrossRef] [PubMed] 13. Bessi, V.; Balestrini, J.; Bagnoli, S.; Mazzeo, S.; Giacomucci, G.; Padiglioni, S.; Piaceri, I.; Carraro, M.; Ferrari, C.; Bracco, L.; et al. Influence of ApoE Genotype and Clock T3111C Interaction with Cardiovascular Risk Factors on the Progression to Alzheimer’s Disease in Subjective Cognitive Decline and Mild Cognitive Impairment Patients. J. Pers. Med. 2020 , 10 , 45. [CrossRef] [PubMed] 14. Scassellati, C.; Ciani, M.; Maj, C.; Geroldi, C.; Zanetti, O.; Gennarelli, M.; Bonvicini, C. Behavioural and Psychological Symptoms of Dementia (BPSD): Clinical characterization and genetic correlates in an Italian Alzheimer Disease cohort. J. Pers. Med. 2020 , 10 , 90. [CrossRef] [PubMed] 15. Khan, M.S.H.; Hegde, V. Obesity and Diabetes Mediated Chronic Inflammation: A Potential Biomarker in Alzheimer’s Disease. J. Pers. Med. 2020 , 10 , 42. [CrossRef] [PubMed] 16. Argentati, C.; Tortorella, I.; Bazzucchi, M.; Emiliani, C.; Morena, M.; Martino, S. The other side of Alzheimer’s Disease: Influence of metabolic disorder features for novel diagnostic biomarkers. J. Pers. Med. 2020 , 10 , 115. [CrossRef] 17. Bell, S.M.; De Marco, M.; Barnes, K.; Shaw, P.J.; Ferraiuolo, L.; Blackburn, D.J.; Mortiboys, H.; Venneri, A. Deficits in Mitochondrial Spare Respiratory Capacity Contribute to the Neuropsychological Changes of Alzheimer’s Disease. J. Pers. Med. 2020 , 10 , 32. [CrossRef] 18. Chiaravalloti, A.; Ricci, M.; Di Biagio, D.; Filippi, L.; Martorana, A.; Schillaci, O. The Brain Metabolic Correlates of the Main Indices of Neuropsychological Assessment in Alzheimer’s Disease. J. Pers. Med. 2020 , 10 , 25. [CrossRef] 19. Picca, A.; Ronconi, D.; Coelho-Junior, H.J.; Calvani, R.; Marini, F.; Biancolillo, A.; Gervasoni, J.; Primiano, A.; Pais, C.; Meloni, E.; et al. The “develOpment of metabolic and functional markers of Dementia IN Older people” (ODINO) Study: Rationale, Design and Methods. J. Pers. Med. 2020 , 10 , 22. [CrossRef] 20. Power, R.; Nolan, J.M.; Prado-Cabrero, A.; Coen, R.; Roche, W.; Power, T.; Howard, A.N.; Mulcahy, R. Targeted Nutritional Intervention for Patients with Mild Cognitive Impairment: The Cognitive impAiRmEnt Study (CARES) Trial 1. J. Pers. Med. 2020 , 10 , 43. [CrossRef] 21. Guglielmi, V.; Quaranta, D.; Mega, I.; Costantini, E.M.; Carrarini, C.; Innocenti, A.; Marra, C. Semantic Priming in Mild Cognitive Impairment and Healthy Subjects: E ff ect of Di ff erent Time of Presentation of Word-Pairs. J. Pers. Med. 2020 , 10 , 57. [CrossRef] [PubMed] 22. Rossini, P.M.; Miraglia, F.; Al ù , F.; Cotelli, M.; Ferreri, F.; Iorio, R.D.; Iodice, F.; Vecchio, F. Neurophysiological Hallmarks of Neurodegenerative Cognitive Decline: The Study of Brain Connectivity as a Biomarker of Early Dementia. J. Pers. Med. 2020 , 10 , 34. [CrossRef] [PubMed] 23. Naughton, S.X.; Raval, U.; Pasinetti, G.M. The Viral Hypothesis in Alzheimer’s Disease: Novel Insights and Pathogen-Based Biomarkers. J. Pers. Med. 2020 , 10 , 74. [CrossRef] [PubMed] Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional a ffi liations. © 2020 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http: // creativecommons.org / licenses / by / 4.0 / ). 4 Journal of Personalized Medicine Review Advantages and Pitfalls in Fluid Biomarkers for Diagnosis of Alzheimer’s Disease Syed Haris Omar 1,2, * and John Preddy 1,2 1 Rural Clinical School, Faculty of Medicine, University of New South Wales, Wagga Wagga, NSW 2650, Australia; j.preddy@unsw.edu.au 2 Murrumbidgee Local Health District, NSW Health, Wagga Wagga, NSW 2650, Australia * Correspondence: haris.omar@unsw.edu.au or syedharisomar@gmail.com Received: 8 June 2020; Accepted: 6 July 2020; Published: 17 July 2020 Abstract: Alzheimer’s disease (AD) is a commonly occurring neurodegenerative disease in the advanced-age population, with a doubling of prevalence for each 5 years of age above 60 years. In the past two decades, there has been a sustained e ff ort to find suitable biomarkers that may not only aide with the diagnosis of AD early in the disease process but also predict the onset of the disease in asymptomatic individuals. Current diagnostic evidence is supportive of some biomarker candidates isolated from cerebrospinal fluid (CSF), including amyloid beta peptide (A β ), total tau ( t -tau), and phosphorylated tau (p-tau) as being involved in the pathophysiology of AD. However, there are a few biomarkers that have been shown to be helpful, such as proteomic, inflammatory, oral, ocular and olfactory in the early detection of AD, especially in the individuals with mild cognitive impairment (MCI). To date, biomarkers are collected through invasive techniques, especially CSF from lumbar puncture; however, non-invasive (radio imaging) methods are used in practice to diagnose AD. In order to reduce invasive testing on the patients, present literature has highlighted the potential importance of biomarkers in blood to assist with diagnosing AD. Keywords: Alzheimer’s disease; cerebrospinal fluid; amyloid beta peptide; total tau; phosphorylated tau; diagnosis 1. Introduction Alzheimer’s disease (AD) is one of the most common neurodegenerative disease in an ageing population. AD is the most common cause of dementia and is characterized by cognitive impairment and the impedance of daily activities, including communication, decision making and behavioral changes [ 1 ]. It has been shown that the frequency of AD doubles for each 5 years of life above the age of sixty years. It is predicted that by 2050, 130 million globally will be symptomatic from AD [ 2 ]. In terms of risk factors, advanced age is the most important risk for the sporadic or late onset of AD as well as the presence of APOE e4 alleles. Inherited mutations in chromosome 11 amyloid precursor protein (APP), Presenilin-1 (PSEN 1 ), and Presenilin-2 (PSEN 2 ) are prevalent in the less common familial form of AD. In addition, women are more prone to AD compared with men, early menopause is also risk factor for AD. Cardiovascular disease and diabetes mellitus type-2 are associated with an increased risk of AD. The exact pathophysiology of AD is still under investigation; however, the deposition of senile plaques, neurofibrillary tangles (NFTs), and astrogliosis are cardinal features [ 3 ]. Moreover, studies have shown that pathological involvement of oxidative stress, neuron degeneration induced synaptic alteration, inflammation and microgliosis are important in the pathogenesis of AD [ 4 ]. Despite almost 3 decades of research into the exact molecular mechanism causing AD, unfortunately, none of the hypothesis completely answers the question. The still amyloid cascade hypothesis suggests a core pathological role of amyloid beta in AD [ 5 ]. The presence of A β peptides in cerebral and peripheral tissues mainly consists of amino acids and their sequences ranging from 1 to 43. A β 42 is very prone J. Pers. Med. 2020 , 10 , 63; doi:10.3390 / jpm10030063 www.mdpi.com / journal / jpm 5 J. Pers. Med. 2020 , 10 , 63 to aggregate and proceed to form the senile plaques found in hippocampus, neocortex and in the cerebrovasculature region [ 6 ]. Another highly aggregated peptide called tau (which undergoes extensive hyperphosphorylation) is responsible for the formation of neurofibrillary tangles inside neurons and ultimately results in extensive brain and nerve damage [ 7 ]. Currently, approved drugs only provided symptomatic relief for patients with AD without modifying the disease or slowing disease progression. However, for the treatment of mild cognitive impairment (MCI), there is no FDA-approved drugs available and suggested to consider o ff -label treatment, such as an acetylcholinesterase (AChE) inhibitor, which has provided a modest impact but is also associated with the risk of side e ff ects. In order to reduce the side e ff ects, research has been undertaken to modify the chemical moiety of drugs with compatible substitutes and also focused on natural products with the potential to act as disease modifying agents [ 8 , 9 ]. Several natural products including curcumin, ginkgolides, resveratrol, oleuropein etc. have been shown to be e ff ective against AD pathology in vitro or in vivo models but have not shown success in randomized trails [ 10 –13 ]. Lifestyle modification, including exercise and dietary modification, especially the Mediterranean diet (MedDi) and Mediterranean-Dietary Approaches to Stop Hypertension (DASH) diet Intervention for Neurological Delay (MIND) diet, have been associated with improved cognition among elderly subjects [14]. It has been shown that pathological changes of AD occur long before the appearance of clinical symptoms. Therefore, it is important to establish a diagnosis as early as possible especially for people above the age 60 years. Biomarkers offer essential tools for AD diagnosis, monitoring, early detection, therapeutic intervention, as well as prevention of inaccurate diagnoses. Body fluid biomarkers in cerebrospinal fluid (CSF) and blood have shown potential for AD diagnosis, individual prognosis and patient stratification. Despite the availability of numerous theoretical and clinical diagnostic tools, AD is still poor diagnosed, especially in the early stage of the disease. AD has a prolonged pre-symptomatic prodromal phase; however, the lack of specific biomarker, procedural and methodological inconsistencies, inconsistent cut-off values as well as a lack of assay standardization, have thwarted attempts to establish a diagnosis and treat AD during this early phase. 2. Search Methods Potential studies were identified in electronic database PubMed, Embase, ScienceDirect, Cochrane Library, SpringerLink, Scopus and Google Scholar using combination of following keywords “Alzheimer’s Disease”, “biomarkers” and “Alzheimer’s disease”, “cerebrospinal fluid”, “CSF”, “invasive biomarkers”, “non-invasive biomarkers”, “plasma biomarkers”, “blood biomarkers”, “plasma amyloid”, “plasma tau”, “inflammatory biomarkers”, “imaging biomarkers”, “proteomic biomarkers”, “salivary biomarkers”, “olfactory biomarkers” and “ocular biomarkers”. Selected studies published between 1990 and May 2020 were included to ensure that all randomized trial, pilot studies, and critical reviews or systematic reviews published evidence on potential Alzheimer’s disease biomarkers for three decades were encompassed. The preclinical studies, in vitro studies, published media, as well as duplicate articles were excluded due to being outside the scope of the clinical study aim. 3. Biomarkers in Alzheimer’s Disease A biomarker is usually characterized by substances (synthetic molecules, specified cells, proteins, enzymes, hormones or genetic material) or imaging finding, which is used as a metric characteristic to indicate the presence of a specific physiological state and may assist with establishing a diagnosis well before a clinical diagnosis can be made. Furthermore, the use of biomarkers is increasingly for assisting with the prognosis and diagnosis of AD, reflected by a tremendous increase in research from 1980 to current time (Figure 1). 6 J. Pers. Med. 2020 , 10 , 63 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 1980-1985 1990-1995 1990-1995 1995-2000 2000-2005 2005-2010 2010-2015 2015-2020 Number of Publications Publication Years Figure 1. Publications statistics for “Biomarkers in Alzheimer’s disease”, source PubMed. On the basis of AD pathogenesis and clinical condition, a set of diagnostic criteria were established in 1984, which was updated by the National Institute on Aging and Alzheimer’s Association (NIA-AA) [ 15 ]. The updated NIA-AA guideline was mainly based upon the pathophysiological advancement in clinical, imaging, and research technologies in AD. Similarly, based upon clinical probable , possible , or definite symptoms, National Institute on Neurological and Communicative Disorder and Stroke and the Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) have also published a diagnostic criteria for AD [ 16 ]. The clinical conditions of AD are considered to fall into three stages; however, some studies have expanded this to 1–5 or 1–7 stages. Of all the stages of AD, the prodromal period has the longest duration. This has resulted in a revision of NIA-AA diagnostic criteria, which are mainly based upon the identification of biomarkers, including CSF and imaging as valid diagnostic tools [ 17 ]. Based on modern diagnosis criteria, three sets of biomarkers are used as diagnostic tools, including A β peptide (A), tau peptide (T) and neurodegeneration (N), which are classified as A / T / N framework (Table 1) of AD diagnosis [17]. Table 1. Biomarkers based upon National Institute on Aging and Alzheimer’s Association (NIA-AA) classification [17]. NIA-AA Classification Alzheimer’s Disease Biomarkers Biomarkers Significance in AD Amyloid (A) aggregates CSF A β 42 , A β 42 / A β 40 ratio & Amyloid PET ↓ CSF A β 42 & A β 42 / A β 40 Tau (T) aggregates CSF phosphorylated tau & Tau PET ↑ CSF p-tau Neurodegeneration (N) CSF total tau & Anatomic MRI FDG PET ↑ t-tau NIA-AA: National Institute on Aging and Alzheimer’s Association; ↑ : increase; ↓ : decrease; CSF: cerebrospinal fluid; A β : β -amyloid; PET: positron emission tomography; FDG: fluorodeoxyglucose; MRI: magnetic resonance imaging. 4. Biomarkers Based upon Alzheimer’s Disease Stages Stage 1: assigned to the individuals who do not have functional impairment but might have cognitive impairment, which can only be detected through neuropsychological sensitive instruments. There is increasing evidence that certain biomarkers can predict the pathological changes at an early preclinical phase, namely the presence of amyloid imaging and a reduced CSF A β 42 concentration. 7 J. Pers. Med. 2020 , 10 , 63 Early diagnosis based on biomarkers may assist with the approval of AD treatment, which could provide clinical benefits and improve outcomes [ 18 ]. Further trials are required to evaluate the reliability of clinical measurement and access the potential improvement with the drug-placebo conclusion. Stage 2: Presence of biomarkers that predict pathophysiological changes of AD, a subtle cognitive e ff ect, but no functional deficits in the patients, which can be detected with the use of sensitive instruments; however, they do not fulfil the criteria for dementia. According to the FDA guidance, sensitive neuropsychological testing should be considered alongside biomarker changes to diagnose AD stage 2 [18]. Stage 3: Pathophysiological biomarkers are present, and patients have started to have di ffi culty in doing some daily tasks which are measurable. This stage of the disease corresponds with mild cognitive impairment, whereas the first two stages are preclinical. Stage 4, Stage 5 and Stage 6: Pathophysiological biomarkers are present with the consecutive stages of mild, moderate, and severe AD dementia with worsening cognitive impairment. Assessment of Stages in AD From the FDA classification of stages in Alzheimer’s disease, stage 1 and 2 should be considered critical and monitored seriously. However, from stage 3 onwards, AD patients have similar pathophysiological biomarkers and ongoing cognitive decline. There are two basic questions that stem from the stages of AD. Which biomarkers may predict the presence of stage 1 and stage 2 AD in individuals? Secondly, from the treatment perspective, how can we establish the clinical e ff ect of current FDA-approved drugs for patients with stage-1 AD, which is preclinical (based on the presence of amyloid and reduced CSF A β 42 concentration) without evidence of cognitive decline? There is a need to evaluate predictive biomarkers and establish whether changes in biomarkers is a predictor of treatment success. 5. Biomarkers through Invasive Diagnostic Methods 5.1. Cerebrospinal Fluid Biomarkers Cerebrospinal fluid (CSF) is a clear liquid that is present in the subarachnoid space and ventricular system of the brain and spinal cord. The volume of CSF in the body varies between 125 and 150 mL. The composition of CSF can demonstrate minor biochemical change in the brain. Currently, CSF is considered an excellent biologic fluid that may contain potential biomarkers for AD, which may be able to identify without going through autopsy or biopsy. Furthermore, the presence and concentration of biomarkers may change in parallel to AD progression. The three most suggestive biomarkers in AD are A β , total tau (t-tau), and phosphorylated tau (p-tau) (Table 2). It has been suggested that CSF biomarkers did not vary with severity with stable levels noted in the follow-up patients with clinical AD [19]. Table 2. Established diagnostic biomarkers in the cerebrospinal fluid CSF of Alzheimer’s disease (AD) a [20] and showed 85% sensitivity cuto ff values for AD dementia diagnosis [21]. Biomarkers Controls (pg / mL) AD (pg / mL) % Sensitivity (AD-Control) % Sensitivity (MCI-Control) A β 42 794 ± 20 < 500 * 73 ( ≥ 75 years) 60 ( ≥ 75 years) tau peptide 136 ± 89 (21–50 years) b 74 ( ≤ 64 years) 65 ( ≤ 64 years) 243 ± 127 (51–70 years) > 450 53 (65–74 years) 49 (65–74 years) 341 ± 171 ( > 71 years) > 600 * 61 ( ≥ 75 years) 46 ( ≥ 75 years) p-tau-181 23 ± 2 > 60 37 ( ≥ 75 years) 30 ( ≥ 75 years) a Data obtained using innogenetics single 96-well ELISA kits. b This is not relevant for sporadic AD, because it is only for patients > 60 years of age. * p < 0.001. 8 J. Pers. Med. 2020 , 10 , 63 5.1.1. CSF A β Biomarker The amyloid isoforms A β 40 and A β 42 concentration in CSF are considered to be the most dependable biomarkers for the diagnosis of the AD disease. The production of both amyloid isoforms A β 40 and A β 42 was 24% higher for mutation carriers than noncarriers in the autosomal dominant AD patients. However, it was suggested that the fractional turnover rate of A β 42 was noted 65% higher in mutation carriers [ 22 ]. Interestingly, it was also reported that there is no change in CSF A β 40 , while it is present in a 10-fold higher concentration than A β 42 in the CSF of AD patients. It was suggested that A β 42 be used as a proxy of total A β concentration. The amyloidogenic protein is found throughout the human body, and studies showed that A β 42 concentrations in CSF often correlate with A β levels in the patient’s brain [ 23 ]. It was found that the A β 42 concentration was significantly reduced in CSF, which is a consequence of its presence in fibrils and plaques in the brains of patients with AD [ 24 – 26 ]. There are variations in quantification; however, it was found that A β 42 concentration declined by 50% in CSF of patients with AD as a result of its deposition in the brain parenchyma [27]. The underlying mechanisms of the reduction CSF A β 42 is not clear; however, few studies have suggested that it is due to the excessive hydrophobic aggregation of A β 42 sequestration in plaques, a reduction in its diffusion from interstitial fluid to CSF and / or decreased A β clearanc