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Background Subjective cognitive decline (SCD) has been proposed as a pre-MCI at-risk condition of Alzheimer’s disease (AD). Current research is focusing on a refined assessment of specific SCD features associated with increased risk for AD, as proposed in the SCD-plus criteria. We developed a structured interview (SCD-I) for the assessment of these features and tested their relationship with AD biomarkers. Methods We analyzed data of 205 cognitively normal participants of the DELCODE study (mean age = 68.9 years; 52% female) with available CSF AD biomarkers (Aß-42, p-Tau181, Aß-42/Tau ratio, total Tau). For each of five cognitive domains (including memory, language, attention, planning, others), a study physician asked participants about the following SCD-plus features: the presence of subjective decline, associated worries, onset of SCD, feeling of worse performance than others of the same age group, and informant confirmation. We compared AD biomarkers of subjects endorsing each of these questions with those who did not, controlling for age. SCD was also quantified by two summary scores: the number of fulfilled SCD-plus features, and the number of domains with experienced decline. Covariate-adjusted linear regression analyses were used to test whether these SCD scores predicted abnormality in AD biomarkers. Results Lower Aß-42 levels were associated with a reported decline in memory and language abilities, and with the following SCD-plus features: onset of subjective decline within 5 years , confirmation of cognitive decline by an informant , and decline-related worries . Furthermore, both quantitative SCD scores were associated with lower Aß42 and lower Aß42/Tau ratio, but not with total Tau or p-Tau181. Conclusions Findings support the usefulness of a criterion-based interview approach to assess and quantify SCD in the context of AD and validate the current SCD-plus features as predictors of AD pathology. While some features seem to be more closely associated with AD biomarkers than others, aggregated scores over several SCD-plus features or SCD domains may be the best predictors of AD pathology.

Alzheimer’s disease (AD) is characterized by β-amyloid plaques and intraneuronal τ aggregation usually associated with cerebral amyloid angiopathy (CAA). Both β-amyloid plaques and CAA deposits contain fibrillar aggregates of the amyloid β-peptide (Aβ). Aβ plaques and CAA develop first in neocortical areas of preclinical AD patients and, then, expand in a characteristic sequence into further brain regions with end-stage pathology in symptomatic AD patients. Aβ aggregates are not restricted to amyloid plaques and CAA. Soluble and several types of insoluble non-plaque- and non-CAA-associated Aβ aggregates have been described. Amyloid fibrils are products of a complex self-assembly process that involves different types of transient intermediates. Amongst these intermediate species are protofibrils and oligomers. Different variants of Aβ peptides may result from alternative processing or from mutations that lead to rare forms of familial AD. These variants can exhibit different self-assembly and aggregation properties. In addition, several post-translational modifications of Aβ have been described that result, for example, in the production of N-terminal truncated Aβ with pyroglutamate modification at position 3 (Aβ N3pE ) or of Aβ phosphorylated at serine 8 (pSer8Aβ). Both Aβ N3pE and pSer8Aβ show enhanced aggregation into oligomers and fibrils. However, the earliest detectable soluble and insoluble Aβ aggregates in the human brain exhibit non-modified Aβ, whereas Aβ N3pE and pSer8Aβ are detected in later stages. This finding indicates the existence of different biochemical stages of Aβ aggregate maturation with pSer8Aβ being related mainly to cases with symptomatic AD. The conversion from preclinical to symptomatic AD could thereby be related to combined effects of increased Aβ concentration, maturation of aggregates and spread of deposits into additional brain regions. Thus, the inhibition of Aβ aggregation and maturation before entering the symptomatic stage of the disease as indicated by the accumulation of pSer8Aβ may represent an attractive treatment strategy for preventing disease progression.

Abstract Introduction There is an urgent need to develop new treatments for Alzheimer's disease (AD) and to understand the drug development process for new AD therapies. Methods We assessed the agents in the AD pipeline as documented in clinicaltrials.gov for phase I, phase II, and phase III, accessed 1/5/2017. Results There are 105 agents in the AD treatment development pipeline, of which 25 agents are in 29 trials in phase I, 52 agents are in 68 trials in phase II, and 28 agents are in 42 trials in phase III. Seventy percent of drugs in the AD pipeline are disease-modifying therapies (DMTs). Fourteen percent are symptomatic cognitive enhancers, and 13% are symptomatic agents addressing neuropsychiatric and behavioral changes (2% have undisclosed mechanisms). Most trials are sponsored by the biopharmaceutical industry. Trials include patients with preclinical AD (cognitively normal with biomarker evidence of AD), prodromal AD (mild cognitive symptoms and biomarker evidence of AD), and AD dementia. Biomarkers are included in many drug development programs particularly those for DMTs. Thirteen of 46 phase II DMT trials have amyloid imaging as an entry criterion, and 10 of 28 phase III trials incorporate amyloid imaging for diagnosis and entry. A large number of participants are needed for AD clinical trials; in total, 54,073 participants are required for trials spanning preclinical AD to AD dementia. When compared with the 2016 pipeline, there are eight new agents in phase I, 16 in phase II, and five in phase III. Discussion The AD drug development pipeline has 105 agents divided among phase I, phase II, and phase III. The trials include a wide range of clinical trial populations, many mechanisms of action, and require a substantial number of clinical trial participants. Biomarkers are increasingly used in patient identification and as outcome measures, particularly in trials of DMTs.

Background Late-life onset neuropsychiatric symptoms are established risk factors for dementia. The mild behavioral impairment (MBI) diagnostic framework was designed to standardize assessment to determine dementia risk better. In this Mini Review, we summarize the emerging clinical and biomarker evidence, which suggests that for some, MBI is a marker of preclinical Alzheimer’s disease. Main MBI is generally more common in those with greater cognitive impairment. In community and clinical samples, frequency is around 10–15%. Mounting evidence in cognitively normal samples links MBI symptoms with known AD biomarkers for amyloid, tau, and neurodegeneration, as well as AD risk genes. Clinical studies have found detectable differences in cognition associated with MBI in cognitively unimpaired people. Conclusion The emerging evidence from biomarker and clinical studies suggests MBI can be an early manifestation of underlying neurodegenerative disease. Future research must now further validate MBI to improve identification of those at the very earliest stages of disease.

INTRODUCTION We investigated whether older adults with subjective memory complaints (SMC) and amyloid‐β accumulation may show clinical progression over 2 years, as measured by resting‐state electroencephalographic (rsEEG), structural magnetic resonance imaging (sMRI), and cognitive variables, depending on educational attainment. METHODS We analyzed these markers in 84 SMC participants from INSIGHT‐Pre‐AD study, grouped by amyloid‐β deposition (18F‐florbetapir positron emission tomography) and educational attainment. RESULTS In amyloid‐negative individuals, higher educational attainment was linked to greater posterior rsEEG alpha activity, possibly reflecting neuroprotective effects. Conversely, amyloid‐positive individuals with higher educational attainment showed reduced posterior rsEEG alpha rhythms and lower parietal cortical thickness, potentially indicating compensatory mechanisms counteracting early amyloidosis and neurodegeneration. No longitudinal changes were found in either group over 2 years. DISCUSSION Education had a stable influence on rsEEG, sMRI, and cognitive markers over 2 years in SMC individuals. Longer follow‐up periods should be used to monitor brain status with those markers. Highlights Education, subjective memory complaint (SMC), and brain amyloid‐β deposition. Stable influence of education on resting‐state electroencephalographic (rsEEG), structural magnetic resonance imaging (sMRI), and cognitive markers over 2 years. Compensatory mechanism of education against early amyloidosis and neurodegeneration. Longer follow‐up periods to monitor brain status in SMC older adults with those markers.

Purpose Preclinical, or asymptomatic, Alzheimer's disease (AD) refers to the presence of positive AD biomarkers in the absence of cognitive deficits. This research concept is being applied to define target populations for clinical drug development. In a prospective community-recruited cohort of cognitively intact older adults, we compared two amyloid imaging markers within subjects: F-18-flutemetamol and C-11-Pittsburgh compound B (PIB). Methods In 32 community-recruited cognitively intact older adults aged between 65 and 80 years, we determined the concordance between binary classification based on F-18-flutemetamol versus C-11-PIB according to semiquantitative assessment (standardized uptake value ratio in composite cortical volume, SUVRcomp) and, alternatively, according to visual reads. We also determined the correlation between F-18-flutemetamol and C-11-PIB SUVR and evaluated how this was affected by the reference region chosen (cerebellar grey matter versus pons) and the use of partial volume correction (PVC) in this population. Results Binary classification based on semiquantitative assessment was concordant between F-18-flutemetamol and C-11-PIB in 94 % of cases. Concordance of blinded binary visual reads between tracers was 84 %. The Spearman correlation between F-18-flutemetamol and C-11-PIB SUVRcomp with cerebellar grey matter as reference region was 0.84, with a slope of 0.98. Correlations in neocortical regions were significantly lower with the pons as reference region. PVC improved the correlation in striatum and medial temporal cortex. Conclusion For the definition of preclinical AD based on F-18-flutemetamol, concordance with C-11-PIB was highest using semiquantitative assessment with cerebellar grey matter as reference region.

Introduction: We investigated whether monthly assessments of a computerized cognitive composite (C3) could aid in the detection of differences in practice effects (PE) in clinically unimpaired (CU) older adults, and whether diminished PE were associated with Alzheimer’s disease (AD) biomarkers and annual cognitive decline. Materials and Methods: N=114 CU participants (age 77.6±5.0, 61% female, MMSE 29±1.2) from the Harvard Aging Brain Study completed the self-administered C3 monthly, at-home, on an iPad for over one year. At baseline, participants underwent in-clinic Preclinical Alzheimer’s Cognitive Composite-5 (PACC5) testing, and a subsample (n=72, age=77.8±4.9, 59% female, MMSE 29±1.3) had one-year follow-up in-clinic PACC5 testing available. Participants had undergone PIB-PET imaging (0.99±1.6 years before at-home baseline) and Flortaucipir PET imaging (n=105, 0.62±1.1 years before at-home baseline). Linear mixed models were used to investigate change over months on the C3 adjusting for age, sex, and years of education, and to extract individual covariate-adjusted slopes over the first three months. We investigated the association of 3-month C3 slopes with global amyloid burden and tau deposition in eight predefined regions of interest, and conducted Receiver Operating Curve analyses to examine how accurately 3-month C3 slopes could identify individuals that showed >0.10 SD annual decline on the PACC-5. Results: Overall, individuals improved on all C3 measures over 12 months (β=0.23, 95%CI=[0.21-0.25], p<.001), but improvement over the first 3 months was greatest (β=0.68, 95%CI=[0.59-0.77], p<.001), suggesting stronger PE over initial repeated exposures. However, lower PE over 3 months were associated with more global amyloid burden (r=-.20, 95%CI=[-0.38 – -0.01], p=.049) and tau deposition in the entorhinal cortex (r=-.38, 95%CI=[-0.54 – -0.19], p<.001) and inferior-temporal lobe (r=-.23, 95%CI=[-0.41 – -0.02], p=.03). 3-month C3 slopes exhibited good discriminative ability to identify PACC-5 decliners (AUC 0.91, 95%CI=[0.84 – 0.98]), which was better than baseline C3 (p<.001) and baseline PACC-5 scores (p=.02). Conclusion: While PE are commonly observed among CU adults, diminished PE over monthly cognitive testing are associated with greater AD biomarker burden and cognitive decline. Our findings imply that unsupervised computerized testing using monthly retest paradigms can provide rapid detection of diminished PE indicative of future cognitive decline in preclinical AD.

Preclinical, or asymptomatic, Alzheimer's disease (AD) refers to the presence of positive AD biomarkers in the absence of cognitive deficits. This research concept is being applied to define target populations for clinical drug development. In a prospective community-recruited cohort of cognitively intact older adults, we compared two amyloid imaging markers within subjects: (18)F-flutemetamol and (11)C-Pittsburgh compound B (PIB). In 32 community-recruited cognitively intact older adults aged between 65 and 80 years, we determined the concordance between binary classification based on (18)F-flutemetamol versus (11)C-PIB according to semiquantitative assessment (standardized uptake value ratio in composite cortical volume, SUVRcomp) and, alternatively, according to visual reads. We also determined the correlation between (18)F-flutemetamol and (11)C-PIB SUVR and evaluated how this was affected by the reference region chosen (cerebellar grey matter versus pons) and the use of partial volume correction (PVC) in this population. Binary classification based on semiquantitative assessment was concordant between (18)F-flutemetamol and (11)C-PIB in 94 % of cases. Concordance of blinded binary visual reads between tracers was 84 %. The Spearman correlation between (18)F-flutemetamol and (11)C-PIB SUVRcomp with cerebellar grey matter as reference region was 0.84, with a slope of 0.98. Correlations in neocortical regions were significantly lower with the pons as reference region. PVC improved the correlation in striatum and medial temporal cortex. For the definition of preclinical AD based on (18)F-flutemetamol, concordance with (11)C-PIB was highest using semiquantitative assessment with cerebellar grey matter as reference region.

Background Blood biomarkers that can be used for preclinical Alzheimer’s disease (AD) diagnosis would enable trial enrollment at a time when the disease is potentially reversible. Here, we investigated plasma neuronal-derived extracellular vesicle (nEV) cargo in patients along the Alzheimer’s continuum, focusing on cognitively normal controls (NCs) with high brain β-amyloid (Aβ) loads (Aβ+). Methods The study was based on the Sino Longitudinal Study on Cognitive Decline project. We enrolled 246 participants, including 156 NCs, 45 amnestic mild cognitive impairment (aMCI) patients, and 45 AD dementia (ADD) patients. Brain Aβ loads were determined using positron emission tomography. NCs were classified into 84 Aβ− NCs and 72 Aβ+ NCs. Baseline plasma nEVs were isolated by immunoprecipitation with an anti-CD171 antibody. After verification, their cargos, including Aβ, tau phosphorylated at threonine 181, and neurofilament light, were quantified using a single-molecule array. Concentrations of these cargos were compared among the groups, and their receiver operating characteristic (ROC) curves were constructed. A subset of participants underwent follow-up cognitive assessment and magnetic resonance imaging. The relationships of nEV cargo levels with amyloid deposition, longitudinal changes in cognition, and brain regional volume were explored using correlation analysis. Additionally, 458 subjects in the project had previously undergone plasma Aβ quantification. Results Only nEV Aβ was included in the subsequent analysis. We focused on Aβ 42 in the current study. After normalization of nEVs, the levels of Aβ 42 were found to increase gradually across the cognitive continuum, with the lowest in the Aβ− NC group, an increase in the Aβ+ NC group, a further increase in the aMCI group, and the highest in the ADD group, contributing to their diagnoses (Aβ− NCs vs. Aβ+ NCs, area under the ROC curve values of 0.663; vs. aMCI, 0.857; vs. ADD, 0.957). Furthermore, nEV Aβ 42 was significantly correlated with amyloid deposition, as well as longitudinal changes in cognition and entorhinal volume. There were no differences in plasma Aβ levels among NCs, aMCI, and ADD individuals. Conclusions Our findings suggest the potential use of plasma nEV Aβ 42 levels in diagnosing AD-induced cognitive impairment and Aβ+ NCs. This biomarker reflects cortical amyloid deposition and predicts cognitive decline and entorhinal atrophy.

Introduction Amyloid beta (Aβ) accumulation is the first pathological hallmark of Alzheimer's disease (AD), and it is associated with altered white matter (WM) microstructure. We aimed to investigate this relationship at a regional level in a cognitively unimpaired cohort. Methods We included 179 individuals from the European Medical Information Framework for AD (EMIF‐AD) preclinAD study, who underwent diffusion magnetic resonance (MR) to determine tract‐level fractional anisotropy (FA); mean, radial, and axial diffusivity (MD/RD/AxD); and dynamic [18F]flutemetamol) positron emission tomography (PET) imaging to assess amyloid burden. Results Regression analyses showed a non‐linear relationship between regional amyloid burden and WM microstructure. Low amyloid burden was associated with increased FA and decreased MD/RD/AxD, followed by decreased FA and increased MD/RD/AxD upon higher amyloid burden. The strongest association was observed between amyloid burden in the precuneus and body of the corpus callosum (CC) FA and diffusivity (MD/RD) measures. In addition, amyloid burden in the anterior cingulate cortex strongly related to AxD and RD measures in the genu CC. Discussion Early amyloid deposition is associated with changes in WM microstructure. The non‐linear relationship might reflect multiple stages of axonal damage.