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Background: The latest NIA‐AA Research Framework for Alzheimer's disease (AD) includes a numerical staging system for individuals in the Alzheimer's continuum. The authors introduce "stage 2" as a transitional stage, characterized by the earliest detectable cognitive or neurobehavioral changes. We aimed to characterize stage 2 in a large German multicentre cohort study. Method: Our analysis included baseline data of N = 349 cognitively healthy participants with available CSF biomarker information from the DZNE Longitudinal Cognitive Impairment and Dementia (DELCODE) study. A subsample of n = 110 participants presented with abnormal Aß42/40 biomarkers and was therefore classified as part of the Alzheimer's continuum. Stage 2 characteristics at baseline were defined by subjective decline with concern in any domain on the SCD interview that persisted for at least 6 months. In addition, subtle impairment of objective cognitive performance (<0,5SD) was derived from factor scores of the DELCODE neuropsychological test battery. Subtle neurobehavioral changes were defined by the presence of at least one neuropsychiatric symptom (NPS) on the Neuropsychiatric Inventory Questionnaire (NPI‐Q). Result: The majority of participants in the Alzheimer's continuum presented with only SCD (22.7%) or both SCD and NPS (20.9%), whereas 12.7% presented with only NPS and 5.5% with only subtle objective cognitive decline. While 10.9% showed all three stage 2 characteristics, 14.5% showed none. The distribution of stage 2 characteristics differed between participants in the Alzheimer's continuum and those with normal Aß42/40 (X2(7)=21.21, p<.01). In addition, the number of fulfilled stage 2 criteria was higher in participants with abnormal Aß42/40 (M=1.41, SD=0.87) compared to those with normal amyloid biomarkers (M=1.10, SD=0.84, t(347)=1.45, p=.001). Conclusion: Our findings provide a systematic overview on stage 2 characteristics in a large cohort of cognitively healthy participants with abnormal AD biomarkers and indicate that both the number of fulfilled stage 2 criteria and their distribution differs from participants outside the Alzheimer's continuum. [ABSTRACT FROM AUTHOR]

Background: White matter hyperintensities (WMH), derived from T2‐weighted MRI, represent a sensitive imaging marker of cerebrovascular disease. To investigate WMH in large‐scale multicenter clinical studies, appropriate automated WMH segmentation algorithms are required. Therefore, prior evaluation of these methods is important, especially due to additional challenges posed by the multicenter acquisition. We assessed performance of different WMH segmentation algorithms compared to manual segmentation (reference) in the multicenter DZNE‐Longitudinal Cognitive Impairment and Dementia Study (DELCODE). Method: WMH segmentations were performed on a preliminary randomly‐selected dataset of 20 participants that included FLAIR images from different diagnostic groups across the AD continuum, various study sites and Siemens 3T scanner models. We used freely‐available and fully‐automated WMH segmentation methods, i.e. LST‐LGA, LST‐LPA, and Sysu_Media, the winner of the MICCAI WMH segmentation challenge. Performance of the algorithms was compared to reference segmentation using volumetric and spatial correspondence indices, intraclass correlation coefficient (ICC), Dice's similarity coefficients (DSC), recall (sensitivity), and precision (positive predictive value), across the whole brain and with respect to the number of detected clusters. Result: Preliminary analyses (n=20, example in Fig.1) indicated that mean WMH volumes, estimated by the algorithms, were lower compared to reference, except for Sysu_Media. Volumetric agreement with reference segmentation was moderate‐to‐high across methods (best: Sysu_Media, ICC=0.9). Mean global DSCs were moderate (best: Sysu_Media, DSC=0.6±0.2), with better performance for larger compared to lower WMH load. Mean number of clusters, detected by the algorithms, was lower compared to reference, except for Sysu_Media that overestimated this value. Mean DSCs for clusters were also moderate for all algorithms (best: Sysu_Media, DSC=0.5±0.2). Mean precision of detected WMH load and number of clusters was higher than mean recall (sensitivity) across all methods, except for Sysu_Media. Conclusion: These preliminary findings show that performance of different automated WMH segmentations is relatively similar. Although most algorithms correctly identify WMH (higher precision), the methods underestimate total WMH load (lower sensitivity). The Sysu_Media algorithm performs best in many indices, however, it yielded lowest precision in number of clusters. Results of the final sample (n=40) will be presented, intended to serve as a method guide for conducting WMH segmentations in the multicenter DELCODE study. [ABSTRACT FROM AUTHOR]

Abstract: Background: In the earliest clinical stages of Alzheimer’s disease (AD) when symptoms are mild, clinical diagnosis can be difficult. AD pathology most likely precedes symptoms. Biomarkers can serve as early diagnostic indicators or as markers of preclinical pathologic change. Candidate biomarkers derived from structural and functional neuroimaging and those measured in cerebrospinal fluid (CSF) and plasma show the greatest promise. Unbiased exploratory approaches, eg, proteomics or cortical thickness analysis, could yield novel biomarkers. The objective of this article was to review recent progress in selected imaging and neurochemical biomarkers for early diagnosis, classification, progression, and prediction of AD. Methods: We performed a survey of recent research, focusing on core biomarker candidates in AD. Results: A number of in vivo neurochemistry and neuroimaging techniques, which can reliably assess aspects of physiology, pathology, chemistry, and neuroanatomy, hold promise as biomarkers. These neurobiologic measures appear to relate closely to pathophysiologic, neuropathologic, and clinical data, such as hyperphosphorylation of tau, amyloid beta (Aβ) metabolism, lipid peroxidation, pattern and rate of atrophy, loss of neuronal integrity, functional and cognitive decline, as well as risk of future decline. Current advances in the neuroimaging of mediotemporal, neocortical, and subcortical areas of the brain of mild cognitive impairment (MCI) and AD subjects are presented. CSF levels of Aβ42, tau, and hyperphosphorylated tau protein (p-tau) can distinguish subjects with MCI who are likely to progress to AD. They also show preclinical alterations that predict later development of early AD symptoms. Studies on plasma Aβ are not entirely consistent, but recent findings suggest that decreased plasma Aβ42 relative to Aβ40 might increase the risk of AD. Increased production of Aβ in aging is suggested by elevation of BACE1 protein and enzyme activity in the brain and CSF of subjects with MCI. CSF tau and p-tau are increased in MCI as well and show predictive value. Other biomarkers might indicate components of a cascade initiated by Aβ, such as oxidative stress or inflammation. These merit further study in MCI and earlier. Conclusions: A number of neuroimaging candidate markers are promising, such as hippocampus and entorhinal cortex volumes, basal forebrain nuclei, cortical thickness, deformation-based and voxel-based morphometry, structural and effective connectivity by using diffusion tensor imaging, tractography, and functional magnetic resonance imaging. CSF Aβ42, BACE1, total tau, and p-tau are substantially altered in MCI and clinical AD. Other interesting novel marker candidates derived from blood are being currently proposed (phase I). Biomarker discovery through proteomic approaches requires further research. Large-scale international controlled multicenter trials (such as the U.S., European, Australian, and Japanese Alzheimer’s Disease Neuroimaging Initiative and the German Dementia Network) are engaged in phase III development of the core feasible imaging and CSF biomarker candidates in AD. Biomarkers are in the process of implementation as primary outcome variables into regulatory guideline documents regarding study design and approval for compounds claiming disease modification. [Copyright &y& Elsevier]

Background: Total tau (T-tau) and β-amyloid(1-42) (Aβ1-42) levels in cerebrospinal fluid (CSF) can differentiate Alzheimer's disease (AD) from normal aging or depressive pseudo-dementia. Differential diagnosis from dementia with Lewy bodies (DLB) in clinical settings is difficult. Methods: The analytical performance of the INNOTEST® PHOSPHO-TAU(181P) assay was validated in terms of selectivity, sensitivity, specificity, precision, robustness, and stability. Clinical utility of the assay alone, or combined with T-tau and Aβ1-42, for discrimination of AD (n=94) from patients suffering from DLB (n=60) or from age-matched control subjects (CS) (n=60) was assessed in a multicenter study. Results: CSF concentrations of tau phosphorylated at threonine 181 (P-tau181P) in AD was significantly higher than in DLB and CS. Discriminant analysis, a classification tree, and logistic regression showed that P-tau181P was the most statistically significant single variable of the three biomarkers for discrimination between AD and DLB. Conclusions: P-tau181P quantification is a robust and reliable assay that may be useful in discriminating AD from DLB. Clin Chem Lab Med 2006;44:1472–80. [ABSTRACT FROM AUTHOR]