Dallas P Veitch1, Michael W Weiner2, Paul S Aisen3, Laurel A Beckett4, Nigel J Cairns5, Robert C Green6, Danielle Harvey4, Clifford R Jack7, William Jagust8, John C Morris9, Ronald C Petersen10, Andrew J Saykin11, Leslie M Shaw12, Arthur W Toga13, John Q Trojanowski14. 1. Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Northern California Institute for Research and Education (NCIRE), Department of Veterans Affairs Medical Center, San Francisco, CA, USA. 2. Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA. Electronic address: michael.weiner@ucsf.edu. 3. Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA. 4. Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA. 5. Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA. 6. Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. 7. Department of Radiology, Mayo Clinic, Rochester, MN, USA. 8. Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA. 9. Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA. 10. Department of Neurology, Mayo Clinic, Rochester, MN, USA. 11. Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA. 12. Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. 13. Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. 14. Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Abstract
INTRODUCTION: The overall goal of the Alzheimer's Disease Neuroimaging Initiative (ADNI) is to validate biomarkers for Alzheimer's disease (AD) clinical trials. ADNI is a multisite, longitudinal, observational study that has collected many biomarkers since 2004. Recent publications highlight the multifactorial nature of late-onset AD. We discuss selected topics that provide insights into AD progression and outline how this knowledge may improve clinical trials. METHODS: We used standard methods to identify nearly 600 publications using ADNI data from 2016 and 2017 (listed in Supplementary Material and searchable at http://adni.loni.usc.edu/news-publications/publications/). RESULTS: (1) Data-driven AD progression models supported multifactorial interactions rather than a linear cascade of events. (2) β-Amyloid (Aβ) deposition occurred concurrently with functional connectivity changes within the default mode network in preclinical subjects and was followed by specific and progressive disconnection of functional and anatomical networks. (3) Changes in functional connectivity, volumetric measures, regional hypometabolism, and cognition were detectable at subthreshold levels of Aβ deposition. 4. Tau positron emission tomography imaging studies detailed a specific temporal and spatial pattern of tau pathology dependent on prior Aβ deposition, and related to subsequent cognitive decline. 5. Clustering studies using a wide range of modalities consistently identified a "typical AD" subgroup and a second subgroup characterized by executive impairment and widespread cortical atrophy in preclinical and prodromal subjects. 6. Vascular pathology burden may act through both Aβ dependent and independent mechanisms to exacerbate AD progression. 7. The APOE ε4 allele interacted with cerebrovascular disease to impede Aβ clearance mechanisms. 8. Genetic approaches identified novel genetic risk factors involving a wide range of processes, and demonstrated shared genetic risk for AD and vascular disorders, as well as the temporal and regional pathological associations of established AD risk alleles. 9. Knowledge of early pathological changes guided the development of novel prognostic biomarkers for preclinical subjects. 10. Placebo populations of randomized controlled clinical trials had highly variable trajectories of cognitive change, underscoring the importance of subject selection and monitoring. 11. Selection criteria based on Aβ positivity, hippocampal volume, baseline cognitive/functional measures, and APOE ε4 status in combination with improved cognitive outcome measures were projected to decrease clinical trial duration and cost. 12. Multiple concurrent therapies targeting vascular health and other AD pathology in addition to Aβ may be more effective than single therapies. DISCUSSION: ADNI publications from 2016 and 2017 supported the idea of AD as a multifactorial disease and provided insights into the complexities of AD disease progression. These findings guided the development of novel biomarkers and suggested that subject selection on the basis of multiple factors may lower AD clinical trial costs and duration. The use of multiple concurrent therapies in these trials may prove more effective in reversing AD disease progression.
INTRODUCTION: The overall goal of the Alzheimer's Disease Neuroimaging Initiative (ADNI) is to validate biomarkers for Alzheimer's disease (AD) clinical trials. ADNI is a multisite, longitudinal, observational study that has collected many biomarkers since 2004. Recent publications highlight the multifactorial nature of late-onset AD. We discuss selected topics that provide insights into AD progression and outline how this knowledge may improve clinical trials. METHODS: We used standard methods to identify nearly 600 publications using ADNI data from 2016 and 2017 (listed in Supplementary Material and searchable at http://adni.loni.usc.edu/news-publications/publications/). RESULTS: (1) Data-driven AD progression models supported multifactorial interactions rather than a linear cascade of events. (2) β-Amyloid (Aβ) deposition occurred concurrently with functional connectivity changes within the default mode network in preclinical subjects and was followed by specific and progressive disconnection of functional and anatomical networks. (3) Changes in functional connectivity, volumetric measures, regional hypometabolism, and cognition were detectable at subthreshold levels of Aβ deposition. 4. Tau positron emission tomography imaging studies detailed a specific temporal and spatial pattern of tau pathology dependent on prior Aβ deposition, and related to subsequent cognitive decline. 5. Clustering studies using a wide range of modalities consistently identified a "typical AD" subgroup and a second subgroup characterized by executive impairment and widespread cortical atrophy in preclinical and prodromal subjects. 6. Vascular pathology burden may act through both Aβ dependent and independent mechanisms to exacerbate AD progression. 7. The APOE ε4 allele interacted with cerebrovascular disease to impede Aβ clearance mechanisms. 8. Genetic approaches identified novel genetic risk factors involving a wide range of processes, and demonstrated shared genetic risk for AD and vascular disorders, as well as the temporal and regional pathological associations of established AD risk alleles. 9. Knowledge of early pathological changes guided the development of novel prognostic biomarkers for preclinical subjects. 10. Placebo populations of randomized controlled clinical trials had highly variable trajectories of cognitive change, underscoring the importance of subject selection and monitoring. 11. Selection criteria based on Aβ positivity, hippocampal volume, baseline cognitive/functional measures, and APOE ε4 status in combination with improved cognitive outcome measures were projected to decrease clinical trial duration and cost. 12. Multiple concurrent therapies targeting vascular health and other AD pathology in addition to Aβ may be more effective than single therapies. DISCUSSION: ADNI publications from 2016 and 2017 supported the idea of AD as a multifactorial disease and provided insights into the complexities of AD disease progression. These findings guided the development of novel biomarkers and suggested that subject selection on the basis of multiple factors may lower AD clinical trial costs and duration. The use of multiple concurrent therapies in these trials may prove more effective in reversing AD disease progression.
Authors: Talia M Nir; Neda Jahanshad; Christopher R K Ching; Ronald A Cohen; Jaroslaw Harezlak; Giovanni Schifitto; Hei Y Lam; Xue Hua; Jianhui Zhong; Tong Zhu; Michael J Taylor; Thomas B Campbell; Eric S Daar; Elyse J Singer; Jeffry R Alger; Paul M Thompson; Bradford A Navia Journal: J Neurovirol Date: 2019-02-14 Impact factor: 2.643
Authors: Jiansong Fang; Andrew A Pieper; Ruth Nussinov; Garam Lee; Lynn Bekris; James B Leverenz; Jeffrey Cummings; Feixiong Cheng Journal: Med Res Rev Date: 2020-07-13 Impact factor: 12.944
Authors: Melina Claussnitzer; Judy H Cho; Rory Collins; Nancy J Cox; Emmanouil T Dermitzakis; Matthew E Hurles; Sekar Kathiresan; Eimear E Kenny; Cecilia M Lindgren; Daniel G MacArthur; Kathryn N North; Sharon E Plon; Heidi L Rehm; Neil Risch; Charles N Rotimi; Jay Shendure; Nicole Soranzo; Mark I McCarthy Journal: Nature Date: 2020-01-08 Impact factor: 49.962