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Journal of Alzheimer's Research and Therapy

Current Issue Volume No: 1 Issue No: 1

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  • JALR. New Journal, Old questions, Fresh insights

    Roberto Paganelli 1  

    1Department of Medicine & Sciences of Aging, University “G. d’Annunzio” of Chieti-Pescara, Italy

    Author Contributions
    Received 30 Nov 2017; Accepted 08 Dec 2017; Published 13 Dec 2017;

    Academic Editor: JALR Desk review, Profesor,US

    Checked for plagiarism: Yes

    Review by: Single-blind

    Copyright ©  2017 Roberto Paganelli, et al.

    Creative Commons License     This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

    Competing interests

    The authors have declared that no competing interests exist.


    Roberto Paganelli (2017) JALR. New Journal, Old questions, Fresh insights. Journal of Alzheimer's Research and Therapy - 1(1):1-5.

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    The responsibility to serve in the editorial board of a new journal in what looks an already crowded arena is a real challenge and also a leap of optimism. The figures tell us that publications in the field of Alzheimer’s disease (AD) have doubled in the past 10 years (from 4,529 in 2007 up to 9,480 until November 2017, timeline of the U.S. National Library of Medicine) and new diagnostic and therapeutic tools are constantly proposed and sometimes introduced in the clinic. The neuroimaging technological advances allow to explore in detail the morphofunctional changes occurring with normal aging, as well as in mild cognitive impairment and in different types of eurodegenerative disorders. Several theories on the chain of events leading to neuronal damage and loss are tested in transgenic mouse models as well as in controlled clinical trials. These are offering more insights on the truly relevant aspects of AD by taking advantage of biochemical and genomic tools for patients selection in the new era of personalized medicine 1, 2, 3.

    AD is the most frequent age-related neurodegenerative disorder, characterized by synaptic dysfunction, neuronal damage and presence of aggregates of amyloid β-protein (Aβ) and tau protein 4. This type dementia is characterized clinically by the loss of memory, multiple cognitive impairments and changes in the personality and behavior. However clinical diagnoses can display significant phenotypic heterogeneity and also in this area recent work has been done to identify the correlates of such diversity 2, 5.

    Genetic evidence, transgenic mice models and biochemical data seem to support the amyloid hypothesis of the pathogenesis of AD: Aβ molecules tend to aggregate to form oligomers, which are extremely toxic and induce neuroinflammation 6. In a rat model the Aβ pathology has been shown to induce neuroimaging and Alzheimer-like profile of biomarker abnormalities 7.

    Therapy based on the amyloid hypothesis include Aβ vaccination and passive antibody treatment, either specific monoclonal antibodies or human pooled immunoglobulins 8, 9, 10, 11, 12, 13 with the common unique aim to reduce Aβ load and prevent its aggregation in soluble oligomers and insoluble fibrils. The failure of immunotherapy trials can be due to the wrong choice of the target, the wrong selection of patients or both, but the most likely cause lies in the defective and/or imprecise tool used to reach the ultimate goal of removing pathogenic aggregates, e.g. antibodies with the wrong spectrum of specificity. However very recent reports allow some more hope in this field 8, 14. Many other approaches to AD therapy have been attempted in transgenic mice models, targeting different receptors/mechanisms 15, 16, 17 and the neurotransmitters’network 18 but also reconsidering the effects of diet and hormones 19, 20. AD models are useful to assess both removal of Aβ and the reduction of neuroinflammation 21 in preclinical studies 22.

    About a quarter to a third of dementia cases can be prevented through the modification of key risk factors including low educational attainment and well known cardiovascular risk factors 23. Following these findings, the prevalence of dementia has been reported to be declining among older US adults between 2000 and 2012 24. However in most regions of the world dementia rates are growing rapidly in relation to population aging 25. The decline in the USA occurred in those older than 65 years and was related to increased number of years in education 24, despite the age- and sex-adjusted increase in the prevalence of hypertension, diabetes, and obesity in the same observation time. A risk score incorporating common genetic variation outside the APOEɛ4 locus may improve AD risk prediction and facilitate risk stratification for prevention trials. Future studies are needed to assess how the risk variables interact together to increase an individual's risk of future dementia, also taking into account comorbidities such as diabetes 26. The field of metabolic studies, including the omics, is particularly promising 27, 28, 29 and recent collaborative studies have contributed to identify different profiles 30. In the absence of effective treatments for dementia a multimodal intervention consisting of diet, exercise, cognitive training and vascular risk monitoring could maintain or even improve cognitive functioning in an at-risk population 31. Exercise has been shown to enhance neurotrophic factor signaling 32.

    The prodromal phase of neurodegenerative disorders with inflammatory features, such as AD,includes mild cognitive impairment (MCI) whose tendency to progress to dementia is not easily captured by a single test 33, 34, 35, 36, 37, 38, 39. This is an area where more studies are needed to validate a set of predictive biochemical, genetic and radiographic determinations 36, 40, 41, 42. Refinements in imaging techniques will help to follow changes from MCI to AD 37, 43. The role of CSF biomarkers has been evaluated 44 and a consensus reached on its usefulness as a supplement to clinical evaluation, particularly in uncertain and atypical cases 45, but it is not yet recommended as a substitute for neuroimaging 46.

    Prevalence of dementias of all types increase with old age, from about 2-3% among those aged 70–75 years to 20–25% among those aged 85 years or more 47. Taken together for the two most frequent types of dementia (AD and Vascular) 48 vascular risk factors such as T2D, hypertension, dietary fat intake, high cholesterol, and obesity have emerged as the most important determinants 49.

    The predisposing factors are being investigated in longitudinal studies as well as in retrospective studies. They reveal a complex picture of interrelated morbidities which need to be assessed with respect to the treatments administered to manage each one. A novel approach to understand AD pathogenic mechanisms is therefore needed. This can generate new models of the dynamic nature of relations among different levels (biochemical, genetic, vascular) and offer new therapeutic candidates which can be targeted by combined treatments and be used to assess disease course. It is in this scenario that the new journal has the potential to contribute to the debate on the ever increasingly complex aspects of AD research and therapy.


    1.Padmanabhan K, Shpanskaya K, Bello G, Doraiswamy P M, Samatova N F. (2017) Toward Personalized Network Biomarkers in Alzheimer's Disease: Computing Individualized Genomic and Protein Crosstalk Maps. Front Aging Neurosci. 9, 315.
    2.Latta C H, Brothers H M, Wilcock D M. (2015) Neuroinflammation in Alzheimer's disease) A source of heterogeneity and target for personalized therapy. , Neuroscience 302, 103-11.
    3.Kosik K S. (2015) Personalized medicine for effective Alzheimer disease treatment. , JAMA Neurol 72(5), 497-8.
    4.Skaper S D, Facci L, Zusso M, Giusti P. (2017) Synaptic Plasticity, Dementia and Alzheimer Disease. , CNS Neurol Disord Drug Targets 16(3), 2-20.
    5.Dong A, Toledo J B, Honnorat N, Doshi J, Varol E et al. (2017) Heterogeneity of neuroanatomical patterns in prodromal Alzheimer's disease: links to cognition, progression and biomarkers. , Brain 140(3), 735-47.
    6.Knight E M, Kim S H, Kottwitz J C, Hatami A, Albay R et al. (2016) Effective anti-Alzheimer Abeta therapy involves depletion of specific Abeta oligomer subtypes. , Neurol Neuroimmunol Neuroinflamm 3(3), 237.
    7.Parent M J, Zimmer E R, Shin M, Kang M S, Fonov V S et al. (2017) Multimodal imaging in rat model recapitulates Alzheimer's Disease biomarkers abnormalities. , J Neurosci
    8.Wang Y, Yan T, Lu H, Yin W, Lin B et al. (2017) Lessons from Anti-Amyloid-beta Immunotherapies in Alzheimer Disease: Aiming at a Moving Target. Neurodegener Dis. 17(6), 242-50.
    9.Wang A, Das P, Switzer R C, Golde TE 3rd, Jankowsky J L. (2011) Robust amyloid clearance in a mouse model of Alzheimer's disease provides novel insights into the mechanism of amyloid-beta immunotherapy. , J Neurosci 31(11), 4124-36.
    10.Vehmas A K, Borchelt D R, Price D L, McCarthy D, Wills-Karp M et al. (2001) beta-Amyloid peptide vaccination results in marked changes in serum and brain Abeta levels in APPswe/PS1DeltaE9 mice, as detected by SELDI-TOF-based ProteinChip technology. , DNA Cell Biol 20(11), 713-21.
    11.Wozniak M A, Itzhaki R F. (2013) Intravenous immunoglobulin reduces beta amyloid and abnormal tau formation caused by herpes simplex virus type 1. , J Neuroimmunol.257(1-2): 7-12.
    12.Wang T, Xie Ji M, Wang S W, Zha J, Zhou W W. (2016) Naturally occurring autoantibodies against Abeta oligomers exhibited more beneficial effects in the treatment of mouse model of Alzheimer's disease than intravenous immunoglobulin. , Neuropharmacology 105, 561-76.
    13.Relkin N R, Thomas R G, Rissman R A, Brewer J B, Rafii M S et al. (2017) A phase 3 trial of IV immunoglobulin for Alzheimer disease. , Neurology 88(18), 1768-75.
    14.Xing H Y, Li B, Peng D, Wang C Y, Wang G Y et al.(2017)A novel monoclonal antibody against the N-terminus of Abeta1-42 reduces plaques and improves cognition in a mouse model of Alzheimer's disease. , PLoS One 12(6), 0180076.
    15.Reddy P H, Manczak M, Kandimalla R. (2017) Mitochondria-targeted small molecule SS31: a potential candidate for the treatment of Alzheimer's disease. , Hum Mol Genet 26(8), 1483-96.
    16.L de Matteis, Martin-Rapun R, JM De la Fuente. (2017) Nanotechnology in Personalized Medicine: A Promising Tool for Alzheimer's Disease Treatment. Curr Med Chem.
    17.Baranger K, Giannoni P, Girard S D, Girot S, Gaven F et al. (2017) Chronic treatments with a 5-HT4 receptor agonist decrease amyloid pathology in the entorhinal cortex and learning and memory deficits in the 5xFAD mouse model of Alzheimer's disease. , Neuropharmacology 126, 128-41.
    18.Kandimalla R, Reddy P H. (2017) Therapeutics of Neurotransmitters in Alzheimer's Disease. , J Alzheimers Dis 57(4), 1049-69.
    19.Sah S K, Lee C, Jang J H, Park G H. (2017) Effect of high-fat diet on cognitive impairment in triple-transgenic mice model of Alzheimer's disease. , Biochem Biophys Res Commun 493(1), 731-6.
    20.Christensen A, Pike C J. (2017) Age-dependent regulation of obesity and Alzheimer-related outcomes by hormone therapy in female 3xTg-AD mice. PLoS One. 12(6), 0178490.
    21.Zhu S, Wang J, Zhang Y, He J, Kong J et al. (2017) The role of neuroinflammation and amyloid in cognitive impairment in an APP/PS1 transgenic mouse model of Alzheimer's disease. , CNS Neurosci Ther 23(4), 310-20.
    22.Sasaguri H, Nilsson P, Hashimoto S, Nagata K, Saito T et al. (2017) APP mouse models for Alzheimer's disease preclinical studies. , EMBO J 36(17), 2473-87. Bruijn RF, Bos M J, Portegies M L, Hofman A, Franco O H et al. (2015) The potential for prevention of dementia across two decades: the prospective, population-based Rotterdam Study. , BMC Med 13, 132.
    24.Langa K M, Larson E B, Crimmins E M, Faul J D, Levine D A et al. (2017) . A Comparison of the Prevalence of Dementia in the United States in2000and2012. JAMA Intern Med 177(1), 51-8.
    25.Kalaria R N, Maestre G E, Arizaga R, Friedland R P, Galasko D et al. (2008) Alzheimer's disease and vascular dementia in developing countries: prevalence, management, and risk factors. , Lancet Neurol 7(9), 812-26.
    26.Secnik J, Cermakova P, Fereshtehnejad S M, Dannberg P, Johnell K et al. (2017) Diabetes in a Large Dementia Cohort: Clinical Characteristics and Treatment From the Swedish Dementia Registry. Diabetes Care. 40(9), 1159-66.
    27.St John-Williams L, Blach C, Toledo J B, Rotroff D M, Kim S et al. (2017) Targeted metabolomics and medication classification data from participants in the ADNI1 cohort. , Sci Data 4, 170140. Oliveira FF, Chen E S, Smith M C, PHF Bertolucci. (2017) Longitudinal lipid profile variations and clinical change in Alzheimer's disease dementia. , Neurosci Lett 646, 36-42.
    29.Chaney A, Bauer M, Bochicchio D, Smigova A, Kassiou M et al. (2017) Longitudinal investigation of neuroinflammation and metabolite profiles in the APPswe xPS1Deltae9 transgenic mouse model of Alzheimer's disease. , J Neurochem
    30.Toledo J B, Arnold M, Kastenmuller G, Chang R, Baillie R A et al. (2017) Metabolic network failures in Alzheimer's disease: A biochemical road map. Alzheimers Dement. 13(9), 965-84.
    31.Rosenberg A, Ngandu T, Rusanen M, Antikainen R, Backman L et al. (2017) Multidomain lifestyle intervention benefits a large elderly population at risk for cognitive decline and dementia regardless of baseline characteristics: The FINGER trial. Alzheimers Dement.
    32.Nigam S M, Xu S, Kritikou J S, Marosi K, Brodin L et al. (2017) Exercise and BDNF reduce Abeta production by enhancing alpha-secretase processing of APP. , J Neurochem 142(2), 286-96.
    33.Viticchi G, Falsetti L, Buratti L, Sajeva G, Luzzi S et al. (2017) Framingham Risk Score and the Risk of Progression from Mild Cognitive Impairment to Dementia. , J Alzheimers Dis 59(1), 67-75.
    34.Sanchez-Catasus C A, Stormezand G N, van Laar PJ, De, Sanchez M A et al. (2017) FDG-PET for Prediction of AD Dementia. in Mild Cognitive Impairment. A Review of the State of the Art with Particular Emphasis on the Comparison with Other Neuroimaging Modalities (MRI and Perfusion SPECT). Curr Alzheimer Res 14(2), 127-42.
    35.Ritchie C, Smailagic N, Noel-Storr A H, Ukoumunne O, Ladds E C et al. (2017) CSF tau and the CSF tau/ABeta ratio for the diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev. 3:CD010803
    36.Lacour A, Espinosa A, Louwersheimer E, Heilmann S, Hernandez I et al. (2017) Genome-wide significant risk factors for Alzheimer's disease: role in progression to dementia due to Alzheimer's disease among subjects with mild cognitive impairment. Mol Psychiatry. 22(1), 153-60.
    37.Iaccarino L, Chiotis K, Alongi P, Almkvist O, Wall A et al. (2017) A Cross-Validation of FDG- and Amyloid-PET Biomarkers in Mild Cognitive Impairment for the Risk Prediction to Dementia due to Alzheimer's Disease in a Clinical Setting. , J Alzheimers Dis 59(2), 603-14.
    38.Cornelis E, Gorus E, Beyer I, Bautmans I, P De Vriendt. (2017) Early diagnosis of mild cognitive impairment and mild dementia through basic and instrumental activities of daily living: Development of a new evaluation tool. , PLoS Med 14(3), 1002250.
    39.Ardekani B A, Bermudez E, Mubeen A M, Bachman A H. (2017) Alzheimer’s Disease Neuroimaging I. Prediction of Incipient Alzheimer’s Disease Dementia in Patients with Mild Cognitive Impairment. , J Alzheimers Dis 55(1), 269-81.
    40.Vanitallie T B. (2013) Preclinical sporadic Alzheimer's disease: target for personalized diagnosis and preventive intervention. Metabolism.62Suppl1: 30-3.
    41.Teijido O, Carril J C, Cacabelos R. (2017) Population-based Study of Risk Polymorphisms Associated with Vascular Disorders and Dementia. Curr Genomics. 18(5), 430-41.
    42.Mak E, Gabel S, Mirette H, Su L, Williams G B et al. (2017) Structural neuroimaging in preclinical dementia: From microstructural deficits and grey matter atrophy to macroscale connectomic changes. Ageing Res Rev. 35, 250-64.
    43.Malpetti M, Ballarini T, Presotto L, Garibotto V, Tettamanti M et al. (2017) Gender differences in healthy aging and Alzheimer's Dementia: A 18 F-FDG-PET study of brain and cognitive reserve. , Hum Brain Mapp 38(8), 4212-27.
    44.Llorens F, Schmitz M, Knipper T, Schmidt C, Lange P et al. (2017) Cerebrospinal Fluid Biomarkers of Alzheimer's Disease Show Different but Partially Overlapping Profile Compared to Vascular Dementia. Front Aging Neurosci. 9, 289.
    45.Simonsen A H, Herukka S K, Andreasen N, Baldeiras I, Bjerke M et al. (2017) Recommendations for CSF AD biomarkers in the diagnostic evaluation of dementia. , Alzheimers Dement 13(3), 274-84.
    46.Sheikh-Bahaei N, Sajjadi S A, Pierce A L. (2017) Current Role for Biomarkers in Clinical Diagnosis of Alzheimer Disease and Frontotemporal Dementia. , Curr Treat Options Neurol 19(12), 46.
    47.Ferri C P, Prince M, Brayne C, Brodaty H, Fratiglioni L et al. (2005) Global prevalence of dementia: a Delphi consensus study. , Lancet 366(9503), 2112-7.
    48.Rizzi L, Rosset I, Roriz-Cruz M. (2014) Global epidemiology of dementia: Alzheimer's and vascular types. Biomed Res Int. 908915.
    49.Villringer A. (2015) The path from obesity and hypertension to dementia. , Adv Exp Med Biol 821, 5.