The goal of this project is to determine relationships among the clinical, cognitive, imaging, genetic and biochemical biomarker characteristics of the entire spectrum of Alzheimer's disease (AD), as pathology evolves from normal aging to very mild symptoms, to mild cognitive impairment (MCI), to dementia. ADNl will inform the neuroscience of AD, identify diagnostic and prognostic markers, identify outcome measures that can be used in clinical trials, and help develop the most effective clinical trial scenarios. ADNI2 continues the currently funded AD Neuroimaging Initiative (ADNI1), a public/private collaboration between academia and industry to study biomarkers of AD as well as a recently funded Grand Opportunities grant that supplements ADNl goals and activities (GO). New aspects of ADNl include enrolling subjects with early MCI (EMCI), F18 amyloid imaging, and obtaining all clinical/cognitive, lumbar puncture CSF and plasma biomarker, and MRI/PET data on all subjects. The goals of this ADNl renewal will be accomplished by: 1) continuing annual clinical/cognitive/MRI follow up of the 476 normal controls and late MCI (LMCI) subjects previously enrolled in ADNI1;2) following the 200 EMCI subjects enrolled in the GO ADNl grant;3) additional enrollment of new healthy controls (n=150), EMCI (n=100 which adds to the 200 subjects enrolled in GO), LMCI (n=150), and AD (n=150) subjects;4) performance of F18 amyloid PET (using F18 AV-45 from AVID, Inc.) on all new subjects enrolled in ADNI2, together with FDG PET, and to obtain a 2nd F18 amyloid PET on all remaining ADNI1, GO, and ADNI2 subjects 2 years after the baseline scan;5) continue to obtain annual clinical/cognitive/blood draw/lumbar puncture for CSF, and MRI on all subjects. All collected data will be processed and analyzed by ADNl investigators including the Biostatistical Core, and made available to all qualified scientists in the world who request a password, without embargo. Hypotheses developed from current ADNl data will be replicated and new hypotheses tested, especially concerning EMCI and F18 amyloid imaging. ADNl spawned large multisite projects in other countries. No other large multisite study in the world addresses these complex issues with the sample size and statistical power of this application.

Public Health Relevance

Alzheimer's disease (AD) causes cognitive impairment and dementia in millions of Americans and costs more than $100 billion/year in the USA. This ADNl project will provide new information which will greatly facilitate design of clinical treatment trials and will help develop new diagnostic techniques which identify AD at an early stage, ultimately leading to effective treatment and prevention of AD. REVIEW OF THE OVERALL PROGRAM

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01AG024904-10
Application #
8724311
Study Section
Special Emphasis Panel (ZAG1)
Program Officer
Ryan, Laurie M
Project Start
2004-09-30
Project End
2015-07-31
Budget Start
2014-09-30
Budget End
2015-07-31
Support Year
10
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Northern California Institute Research & Education
Department
Type
DUNS #
City
San Francisco
State
CA
Country
United States
Zip Code
94121
Amoroso, Nicola; Diacono, Domenico; La Rocca, Marianna et al. (2018) Salient networks: a novel application to study Alzheimer disease. Biomed Eng Online 17:162
Ning, Kaida; Chen, Bo; Sun, Fengzhu et al. (2018) Classifying Alzheimer's disease with brain imaging and genetic data using a neural network framework. Neurobiol Aging 68:151-158
Katako, Audrey; Shelton, Paul; Goertzen, Andrew L et al. (2018) Machine learning identified an Alzheimer's disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson's disease dementia. Sci Rep 8:13236
Xie, Long; Das, Sandhitsu R; Wisse, Laura E M et al. (2018) Early Tau Burden Correlates with Higher Rate of Atrophy in Transentorhinal Cortex. J Alzheimers Dis 62:85-92
Lin, Qi; Rosenberg, Monica D; Yoo, Kwangsun et al. (2018) Resting-State Functional Connectivity Predicts Cognitive Impairment Related to Alzheimer's Disease. Front Aging Neurosci 10:94
Li, Kaicheng; Luo, Xiao; Zeng, Qingze et al. (2018) Aberrant functional connectivity network in subjective memory complaint individuals relates to pathological biomarkers. Transl Neurodegener 7:27
Swanson, Ashley; Wolf, Tovah; Sitzmann, Alli et al. (2018) Neuroinflammation in Alzheimer's disease: Pleiotropic roles for cytokines and neuronal pentraxins. Behav Brain Res 347:49-56
Jang, Jae-Won; Park, Jeong Hoon; Kim, Seongheon et al. (2018) A 'Comprehensive Visual Rating Scale' for predicting progression to dementia in patients with mild cognitive impairment. PLoS One 13:e0201852
Thomas, Kelsey R; Eppig, Joel; Edmonds, Emily C et al. (2018) Word-list intrusion errors predict progression to mild cognitive impairment. Neuropsychology 32:235-245
Girard, Hugo; Potvin, Olivier; Nugent, Scott et al. (2018) Faster progression from MCI to probable AD for carriers of a single-nucleotide polymorphism associated with type 2 diabetes. Neurobiol Aging 64:157.e11-157.e17

Showing the most recent 10 out of 1666 publications