Citations

Introduction The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. Methods We used standard searches to find publications using ADNI data. Results (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β‐amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white‐matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion‐like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by “classic” AD pathology but also by normal biomarkers, accelerated decline, and suspected non‐Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. Discussion Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.

BACKGROUND: To examine whether exposure to general anesthesia for procedures at age ≥40 years is associated with prevalent mild cognitive impairment (MCI) in the elderly. METHODS: A case–control study nested within a population-based cohort. Olmsted County, Minnesota, residents, aged 70–91 years, underwent baseline evaluations that included the Clinical Dementia Rating scale, a neurologic evaluation, and neuropsychologic testing. Individuals identified with MCI (cases) at enrollment were matched 1:2 on age, sex, education, and apolipoprotein genotype with participants who were cognitively normal at the time of the index visit. Medical records from age 40 years until the index visit were reviewed to determine exposures to general anesthesia. Conditional logistic regression, taking into account the matched set study design and adjusting for MCI risk factors, was used to assess whether exposure to anesthesia after the age of 40 years was associated with prevalent MCI. RESULTS: A total of 387 Mayo Clinic Study of Aging participants (219 males, 168 females) were diagnosed with MCI at enrollment with mean age of 81 ± 5 years. Exposure to general anesthesia after the age of 40 years was not significantly associated with prevalent MCI when analyzed as a dichotomous variable (any versus none, adjusted odds ratio, 0.97 [95% confidence interval, 0.68–1.40]) or the number of exposures (odds ratio, 1.13 [0.74–1.72], 0.81 [0.53–1.22], and 1.03 [0.67–1.58] for 1, 2–3, and ≥4 exposures, respectively, with no exposure as the reference). Similar results were obtained for exposure to anesthesia after the age of 60 years and during 5, 10, and 20 years before the first visit. CONCLUSIONS: Exposure to general anesthesia for procedures at age ≥40 years was not associated with prevalent MCI in the elderly.

Abstract Introduction Traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) have previously been reported to be associated with increased risk of Alzheimer's disease (AD). We are using biomarkers to study Vietnam Veterans with/without mild cognitive impairment with a history of at least one TBI and/or ongoing PTSD to determine whether these contribute to the development of AD. Methods Potential subjects identified by Veterans Administration records underwent an initial telephone screen. Consented subjects underwent clinical evaluation, lumbar puncture, structural magnetic resonance imaging, and amyloid positron emission tomography (PET) scans. Results We observed worse cognitive functioning in PTSD and TBI + PTSD groups, worse global cognitive functioning in the PTSD group, lower superior parietal volume in the TBI + PTSD group, and lower amyloid positivity in the PTSD group, but not the TBI group compared to controls without TBI/PTSD. Medial temporal lobe atrophy was not increased in the PTSD and/or TBI groups. Discussion Preliminary results do not indicate that TBI or PTSD increase the risk for AD measured by amyloid PET. Additional recruitment, longitudinal follow‐up, and tau‐PET scans will provide more information in the future.

Abstract Introduction Genetic loci for Alzheimer's disease (AD) have been identified in whites of European ancestry, but the genetic architecture of AD among other populations is less understood. Methods We conducted a transethnic genome‐wide association study (GWAS) for late‐onset AD in Stage 1 sample including whites of European Ancestry, African‐Americans, Japanese, and Israeli‐Arabs assembled by the Alzheimer's Disease Genetics Consortium. Suggestive results from Stage 1 from novel loci were followed up using summarized results in the International Genomics Alzheimer's Project GWAS dataset. Results Genome‐wide significant (GWS) associations in single‐nucleotide polymorphism (SNP)–based tests ( P < 5 × 10 −8 ) were identified for SNPs in PFDN1/HBEGF , USP6NL/ECHDC3 , and BZRAP1‐AS1 and for the interaction of the (apolipoprotein E) APOE ε4 allele with NFIC SNP. We also obtained GWS evidence ( P < 2.7 × 10 −6 ) for gene‐based association in the total sample with a novel locus, TPBG ( P = 1.8 × 10 −6 ). Discussion Our findings highlight the value of transethnic studies for identifying novel AD susceptibility loci.

AbstractIntroductionBiomarkers for Alzheimer's disease (AD) pathophysiology have been developed that focus on various levels of brain organization. However, no robust biomarker of large‐scale network failure has been developed. Using the recently introduced cascading network failure model of AD, we developed the network failure quotient (NFQ) as a biomarker of this process.MethodsWe developed and optimized the NFQ using our recently published analyses of task‐free functional magnetic resonance imaging data in clinically normal (n = 43) and AD dementia participants (n = 28) from the Alzheimer's Disease Neuroimaging Initiative. The optimized NFQ (oNFQ) was then validated in a cohort spanning the AD spectrum from the Mayo Clinic (n = 218).ResultsThe oNFQ (d = 1.25, 95% confidence interval [1.25, 1.26]) had the highest effect size for differentiating persons with AD dementia from clinically normal participants. The oNFQ measure performed similarly well on the validation Mayo Clinic sample (d = 1.44, 95% confidence interval [1.43, 1.44]). The oNFQ was also associated with other available key biomarkers in the Mayo cohort.DiscussionThis study demonstrates a measure of functional connectivity, based on a cascading network failure model of AD, and was highly successful in identifying AD dementia. A robust biomarker of the large‐scale effects of AD pathophysiology will allow for richer descriptions of the disease process and its modifiers, but is not currently suitable for discriminating clinical diagnostic categories. The large‐scale network level may be one of the earliest manifestations of AD, making this an attractive target for continued biomarker development to be used in prevention trials.