Search for Clinical Markers Could? Transform Alzheimer's Drug Research

The pilot European Alzheimer's Disease Neuroimaging Initiative of the European Alzheimer's Disease Consortium

The Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The study aimed to enroll 400 subjects with early mild cognitive impairment (MCI), 200 subjects with early AD, and 200 normal control subjects; $67 million funding was provided by both the public and private sectors, including the National Institute on Aging, 13 pharmaceutical companies, and 2 foundations that provided support through the Foundation for the National Institutes of Health. This article reviews all papers published since the inception of the initiative and summarizes the results as of February 2011. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimers Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, CSF biomarkers, and clinical tests; (4) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects, and are leading candidates for the detection of AD in its preclinical stages; (5) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Baseline cognitive and/or MRI measures generally predicted future decline better than other modalities, whereas MRI measures of change were shown to be the most efficient outcome measures; (6) the confirmation of the AD risk loci CLU, CR1, and PICALM and the identification of novel candidate risk loci; (7) worldwide impact through the establishment of ADNI-like programs in Europe, Asia, and Australia; (8) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker data with clinical data from ADNI to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (9) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world. The ADNI study was extended by a 2-year Grand Opportunities grant in 2009 and a renewal of ADNI (ADNI-2) in October 2010 through to 2016, with enrollment of an additional 550 participants.

Obesity is linked with lower brain volume in 700 AD and MCI patients.

Previous studies have highlighted that decreased hippocampal volume, an early neural correlate of dementia, is commonly observed in patients with mild cognitive impairment (MCI). However, it is unclear whether neurodegenerative and resultant clinical trajectories are accelerated in MCI patients with concomitant depressive symptoms, leading to a faster conversion to dementia stages than those who are not depressed. No longitudinal study has investigated whether depressed amnestic MCI (DEP+aMCI) patients show an earlier onset of progression to dementia than non-depressed amnestic MCI (DEP-aMCI) patients and whether progressive hippocampal volume reductions are related in the conversion process. Using data from Alzheimer's Disease Neuroimaging Initiative, we examined 2-year follow-up data from 38 DEP+aMCI patients and 38 matched DEP-aMCI patients and compared their ages of conversion from aMCI to AD and trajectories of progressive hippocampal volume changes. DEP+ and DEP- patients were defined as having baseline Geriatric Depression Scale scores of 5 or above and 0, respectively. DEP+ converters showed earlier ages of conversion to dementia (p = 0.009) and greater left hippocampal volume loss than both DEP- converters and DEP+ non-converters over the 2-year period (p = 0.003, p = 0.001, respectively). These findings could not be explained by changes in total brain volume, differences in their clinical symptoms of dementia, daily functioning, or apolipoprotein E4 genotypes. No difference in conversion rate to dementia or progressive hippocampal volume change was found between DEP+ patients and DEP-patients, which suggested depressive symptoms themselves may not lead to progression of dementia from MCI. In conclusion, there is a synergistic effect of depressive symptoms and smaller left hippocampal volume in MCI patients that accelerates conversion to dementia.

High Activities of BACE1 in Brains with Mild Cognitive Impairment

Background and Aims The glymphatic system is a network of extracellular spaces between neurons, glial cells, and capillaries that promotes the elimination of soluble molecules from the brain. Its dysfunction is probably relevant for neurodegenerative diseases such as Alzheimer's disease (AD). It is widely accepted that cognitive impairment accompanies chronic kidney disease (CKD). CKD is also a risk factor for dementia. However, the role of the glymphatic system in this process is unknown. A recent method to study the glymphatic system in human subjects has been proposed based on Diffusion Tensor Imaging (DTI) data and water diffusion calculation along with perivascular spaces. This approach is based on calculating a diffusion index named ALPS and showed that the glymphatic flow is reduced in MCI. Method To analyze the role of glymphatic system in CKD patients, we took advantage of the Alzheimer's Disease Neuroimaging Initiative (ADNI). ADNI is a longitudinal multicenter study helping researchers to monitor Alzheimer's disease and Mild Cognitive Impairment (MCI) progression. This database has a cohort of control patients and MCI patients, among which several patients with CKD stage II-III were identifiable from the creatinine values. Patients with Alzheimer's disease were excluded for this study. Among the control and MCI patients, we identified 12 CKD patients and pair-matched 12 non-CKD patients comparable for age, gender, and MoCA score. Magnetic resonance data with DTI sequences were retrieved for all patients, and the glymphatic system was characterized by the ALPS index. Tensor values were calculated using the FSL software; the diffusion values were calculated on tensor images using the ImageJ software. Differences in ALPS between CKD and non-CKD patients with and without MCI were tested. Results Analysis of DTI data confirmed that control patients without CKD had lower ALPS values when MCI was present compared to the non-MCI patients, suggesting a reduction of water diffusion in the glymphatic system. However, the presence of CKD had a different effect: in the absence of MCI, CKD did not modify ALPS values compared to non-CKD patients. At variance, in patients with MCI, CKD resulted in a significant increase of water diffusion in the glymphatic system compared to the controls. Conclusion In this preliminary study, MCI and CKD exerted opposite effects on the diffusion of water within the glymphatic system: MCI was accompanied by a reduction of water diffusion whereas CKD by an increased diffusion in the glymphatic spaces. It is possible that small modification of water balance in CKD may be responsible for the increased diffusion of water in glymphatics in CKD. Further studies are needed to verify whether this unexpected phenomenon may modify cognitive function with a mechanism rather different from Alzheimer's disease.

The close correlation between abnormally low pre-mortem cerebrospinal fluid (CSF) concentrations of amyloid-β1-42 (Aβ(1-42)) and plaque burden measured by amyloid imaging as well as between pathologically increased levels of CSF tau and the extent of neurodegeneration measured by MRI has led to growing interest in using these biomarkers to predict the presence of AD plaque and tangle pathology. A challenge for the widespread use of these CSF biomarkers is the high variability in the assays used to measure these analytes which has been ascribed to multiple pre-analytical and analytical test performance factors. To address this challenge, we conducted a seven-center inter-laboratory standardization study for CSF total tau (t-tau), phospho-tau (p-tau(181)) and Aβ(1-42) as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI). Aliquots prepared from five CSF pools assembled from multiple elderly controls (n = 3) and AD patients (n = 2) were the primary test samples analyzed in each of three analytical runs by the participating laboratories using a common batch of research use only immunoassay reagents (INNO-BIA AlzBio3, xMAP technology, from Innogenetics) on the Luminex analytical platform. To account for the combined effects on overall precision of CSF samples (fixed effect), different laboratories and analytical runs (random effects), these data were analyzed by mixed-effects modeling with the following results: within center %CV 95% CI values (mean) of 4.0-6.0% (5.3%) for CSF Aβ(1-42); 6.4-6.8% (6.7%) for t-tau and 5.5-18.0% (10.8%) for p-tau(181) and inter-center %CV 95% CI range of 15.9-19.8% (17.9%) for Aβ(1-42), 9.6-15.2% (13.1%) for t-tau and 11.3-18.2% (14.6%) for p-tau(181). Long-term experience by the ADNI biomarker core laboratory replicated this degree of within-center precision. Diagnostic threshold CSF concentrations for Aβ(1-42) and for the ratio t-tau/Aβ(1-42) were determined in an ADNI independent, autopsy-confirmed AD cohort from whom ante-mortem CSF was obtained, and a clinically defined group of cognitively normal controls (NCs) provides statistically significant separation of those who progressed from MCI to AD in the ADNI study. These data suggest that interrogation of ante-mortem CSF in cognitively impaired individuals to determine levels of t-tau, p-tau(181) and Aβ(1-42), together with MRI and amyloid imaging biomarkers, could replace autopsy confirmation of AD plaque and tangle pathology as the "gold standard" for the diagnosis of definite AD in the near future.

Brain controllability distinctiveness between depression and cognitive impairment

BackgroundNeuropsychiatric symptoms are behavioural manifestations highly prevalent along the Alzheimer’s disease (AD) continuum, including at the stage of Mild Cognitive Impairment (MCI). Although various projects have investigated the factors that underpin these symptoms, the most stable clustering pattern is still matter for debate; furthermore, to our knowledge, no study has investigated, longitudinally, how clusters might change due to development of AD pathology. Therefore, our objective is to investigate neuropsychiatric clusters over time, in a large sample of MCI and AD dementia patients.MethodsSamples were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu), using data from the Neuropsychiatric Inventory (NPI), from the baseline assessment visit, followed up yearly until data recorded at month 72. The samples included MCI and AD dementia patients (based on ADNI’s inclusion criteria), presenting with at least one neuropsychiatric symptom at the time‐point selected. Using SPSS (Version 28), we performed a series of exploratory principal components analyses (PCA), Varimax rotation, and principal axis factor analyses (FA), comparing Promax and Direct Oblimin rotations.ResultsThe best‐fitting structure was interpreted at each time‐point, based on: Eigenvalues>1, items’ loadings>0.4, scree plot patterns, minimum 40% of variance explained by the model, and independence of factors. Results from both PCA and FA indicated that a unique structure could not be identified, as factors were not stable over time; however, some symptoms tended to load on the same factors across most measurements (i.e. delusions with hallucinations; agitation with depression, anxiety and irritability; to a lesser extent elation with disinhibition; apathy with aberrant motor behaviour, sleep disturbances and appetite disorder).ConclusionsThe available evidence reveals that factors underlying the neuropsychiatric symptoms in a sample of AD and MCI patients are not consistent across the time‐points. However, some symptoms tend to co‐occur across time, with implications, for example, that an investigation into one of those symptoms should consider the presence of the other; furthermore, as clear theoretically driven factors are not distinctively identified at every time‐point, this illustrates the potential importance of sample selection (e.g., disease stage, and/or heterogeneity) on studies of neuropsychiatric symptoms.

BackgroundEarly prevention of Alzheimer’s disease (AD) is a feasible way to delay AD onset and progression. Information on AD prediction at the individual patient level will be useful in AD prevention. In this study, we aim to develop risk models for predicting AD onset at individual level using optimal set of predictors from multiple features.MethodsA total of 487 cognitively normal (CN) individuals and 796 mild cognitive impairment (MCI) patients were included from Alzheimer's Disease Neuroimaging Initiative. All the participants were assessed for clinical, cognitive, magnetic resonance imaging and cerebrospinal fluid (CSF) markers and followed for mean periods of 5.6 years for CN individuals and 4.6 years for MCI patients to ascertain progression from CN to incident prodromal stage of AD or from MCI to AD dementia. Least Absolute Shrinkage and Selection Operator Cox regression was applied for predictors selection and model construction.ResultsDuring the follow-up periods, 139 CN participants had progressed to prodromal AD (CDR ≥ 0.5) and 321 MCI patients had progressed to AD dementia. In the prediction of individual risk of incident prodromal stage of AD in CN individuals, the AUC of the final CN model was 0.81 within 5 years. The final MCI model predicted individual risk of AD dementia in MCI patients with an AUC of 0.92 within 5 years. The models were also associated with longitudinal change of Mini-Mental State Examination (p < 0.001 for CN and MCI models). An Alzheimer’s continuum model was developed which could predict the Alzheimer’s continuum for individuals with normal AD biomarkers within 3 years with high accuracy (AUC = 0.91).ConclusionsThe risk models were able to provide personalized risk for AD onset at each year after evaluation. The models may be useful for better prevention of AD.

Distinct CSF α-synuclein aggregation profiles associated with Alzheimer's disease phenotypes and MCI-to-AD conversion.

Characterizing Alzheimer's disease using a hypometabolic convergence index

Use of an FDG-PET derived hypometabolic convergence index enrichment strategy to reduce sample sizes in Alzheimer's disease clinical trials: findings from the Alzheimer's Disease Neuroimaging Initiative (ADNI)

To quantify and investigate the interactions between multimodal MRI/positron emission tomography (PET) imaging metrics in elderly patients with early Alzheimer's disease (AD), mild cognitive impairment (MCI) and healthy controls. Thirteen early AD, 17 MCI patients, and 14 age-matched healthy aging controls from the Alzheimer's Disease Neuroimaging Initiative database were selected based on availability of data. Default mode network (DMN) functional connectivity and fractional amplitude of low frequency fluctuation (fALFF) were obtained for resting state functional MRI (RS-fMRI). White matter lesion load (WMLL) was quantified from MRI T2-weighted FLAIR images. Amyloid deposition with PET [(18)F]-Florbetapir tracer and metabolism of glucose by means of [(18)F]-fluoro-2-deoxyglucose (FDG) images were quantified using ratio of standard uptake values (rSUV). Whole-brain WMLL and amyloid deposition were significantly higher (P < 0.005) in MCI and AD patients compared with controls. RS-fMRI results showed significantly reduced (corrected P < 0.05) DMN connectivity and altered fALFF activity in both MCI and AD groups. FDG uptake results showed hypometabolism in AD and MCI patients compared with controls. Correlations (P < 0.05) were found between WMLL and amyloid load, FDG uptake and amyloid load, as well as between amyloid load (rSUV) and fALFF. Our quantitative results of four MRI and PET imaging metrics (fALFF/DMN, WMLL, amyloid, and FDG rSUV values) agree with published values. Significant correlations between MRI metrics, including WMLL/functional activity and PET amyloid load suggest the potential of MRI and PET-based biomarkers for early detection of AD.