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Objectives: The aim of the current study was to investigate the health-related quality of life (HRQol) of the family caregiver in MCI, explore possible determinants and study possible differences with mild dementia. Methods: This secondary data analysis included 145 persons with MCI and 154 persons with dementia and their family caregivers from two Dutch cohort studies. HRQoL was measured with the VAS of the EuroQol-5D-3L version. Regressions analyses were conducted to examine potential demographic and clinical determinants of the caregiver’s HRQoL. Results: The mean EQ5D-VAS in family caregivers of persons with MCI was 81.1 (SD 15.7), and did not significantly differ from family caregivers in mild dementia (81.9 (SD 13.0)). In MCI, patient measurements were not significantly associated with caregiver mean EQ5D-VAS. Concerning caregiver characteristics, being a spouse and a lower educational level were associated with a lower mean EQ5D-VAS (in a multiple linear regression model: unstandardized B −8.075, p = 0.013 and unstandardized B −6.162, p = 0.037 resp.). In mild dementia, the NPI item irritability showed an association with caregiver EQ5D-VAS in bivariate linear regression analyses. Conclusion: Results indicate that especially family caregiver characteristics seem to influence family caregiver HRQoL in MCI. Future research should include other potential determinants such as burden, coping strategies and relationship quality.

Importance: One characteristic histopathological event in Alzheimer disease (AD) is cerebral amyloid aggregation, which can be detected by biomarkers in cerebrospinal fluid (CSF) and on positron emission tomography (PET) scans. Prevalence estimates of amyloid pathology are important for health care planning and clinical trial design.Objective: To estimate the prevalence of amyloid abnormality in persons with normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia and to examine the potential implications of cutoff methods, biomarker modality (CSF or PET), age, sex, APOE genotype, educational level, geographical region, and dementia severity for these estimates.Design, Setting, and Participants: This cross-sectional, individual-participant pooled study included participants from 85 Amyloid Biomarker Study cohorts. Data collection was performed from January 1, 2013, to December 31, 2020. Participants had normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia. Normal cognition and subjective cognitive decline were defined by normal scores on cognitive tests, with the presence of cognitive complaints defining subjective cognitive decline. Mild cognitive impairment and clinical AD dementia were diagnosed according to published criteria.Exposures: Alzheimer disease biomarkers detected on PET or in CSF.Main Outcomes and Measures: Amyloid measurements were dichotomized as normal or abnormal using cohort-provided cutoffs for CSF or PET or by visual reading for PET. Adjusted data-driven cutoffs for abnormal amyloid were calculated using gaussian mixture modeling. Prevalence of amyloid abnormality was estimated according to age, sex, cognitive status, biomarker modality, APOE carrier status, educational level, geographical location, and dementia severity using generalized estimating equations.Results: Among the 19 097 participants (mean [SD] age, 69.1 [9.8] years; 10 148 women [53.1%]) included, 10 139 (53.1%) underwent an amyloid PET scan and 8958 (46.9%) had an amyloid CSF measurement. Using cohort-provided cutoffs, amyloid abnormality prevalences were similar to 2015 estimates for individuals without dementia and were similar across PET- and CSF-based estimates (24%; 95% CI, 21%-28%) in participants with normal cognition, 27% (95% CI, 21%-33%) in participants with subjective cognitive decline, and 51% (95% CI, 46%-56%) in participants with mild cognitive impairment, whereas for clinical AD dementia the estimates were higher for PET than CSF (87% vs 79%; mean difference, 8%; 95% CI, 0%-16%; P = .04). Gaussian mixture modeling-based cutoffs for amyloid measures on PET scans were similar to cohort-provided cutoffs and were not adjusted. Adjusted CSF cutoffs resulted in a 10% higher amyloid abnormality prevalence than PET-based estimates in persons with normal cognition (mean difference, 9%; 95% CI, 3%-15%; P = .004), subjective cognitive decline (9%; 95% CI, 3%-15%; P = .005), and mild cognitive impairment (10%; 95% CI, 3%-17%; P = .004), whereas the estimates were comparable in persons with clinical AD dementia (mean difference, 4%; 95% CI, -2% to 9%; P = .18).Conclusions and Relevance: This study found that CSF-based estimates using adjusted data-driven cutoffs were up to 10% higher than PET-based estimates in people without dementia, whereas the results were similar among people with dementia. This finding suggests that preclinical and prodromal AD may be more prevalent than previously estimated, which has important implications for clinical trial recruitment strategies and health care planning policies.

Additional file 2: Figure S1. Longitudinal change in CSF total tau. Figure S2. Enrichment of synaptic processes in individuals with AD according to t-tau status. Figure S3. Enriched GO biological processes and SUZ12 and REST transcription factors associated with proteins that differed between AD individuals with increased t-tau and normal t-tau and proteins that changed with disease severity.

Introduction The evidence for characteristics of persons with subjective cognitive decline (SCD) associated with amyloid positivity is limited. Methods In 1640 persons with SCD from 20 Amyloid Biomarker Study cohort, we investigated the associations of SCD-specific characteristics (informant confirmation, domain-specific complaints, concerns, feelings of worse performance) demographics, setting, apolipoprotein E gene (APOE) epsilon 4 carriership, and neuropsychiatric symptoms with amyloid positivity. Results Between cohorts, amyloid positivity in 70-year-olds varied from 10% to 76%. Only older age, clinical setting, and APOE epsilon 4 carriership showed univariate associations with increased amyloid positivity. After adjusting for these, lower education was also associated with increased amyloid positivity. Only within a research setting, informant-confirmed complaints, memory complaints, attention/concentration complaints, and no depressive symptoms were associated with increased amyloid positivity. Feelings of worse performance were associated with less amyloid positivity at younger ages and more at older ages. Discussion Next to age, setting, and APOE epsilon 4 carriership, SCD-specific characteristics may facilitate the identification of amyloid-positive individuals.

BACKGROUND: Higher anxiety levels in older adults are associated with worse executive functioning and an increased risk for dementia. In this study individual anxiety disorders and clinically relevant generalized anxiety symptoms are studied in relation to multiple cognitive domains.METHOD: This cross-sectional study includes 7344 community-dwelling participants of The Maastricht Study aged 40-75 years and oversampling of type 2 diabetes. Panic disorder with and without agoraphobia, agoraphobia and lifetime panic disorder were measured with the Mini International Neuropsychiatric Interview. Generalized anxiety symptoms were measured with the Generalized Anxiety Disorder 7-item scale (GAD-7). Multiple cognitive domains (executive functioning, memory and processing speed) and cognitive impairment were assessed. Multivariable linear and logistic regression analyses were used with adjustment for potential confounders. Interaction analyses were performed to test the moderation of age, sex and type 2 diabetes (due to oversampling).RESULTS: Agoraphobia was associated with worse scores on all cognitive domains (range B = -0.12 to -0.10; range 95%CI = -0.20 to -0.04) and with higher odds of cognitive impairment (OR = 1.51, 95%CI = 1.18-1.93). High scores on the GAD-7 were associated with worse scores on processing speed (B = -0.11, 95%CI = -0.20 to -0.03) and higher odds of cognitive impairment (OR = 1.42, 95%CI = 1.02-1.97). Panic disorder was significantly associated with worse scores on memory tasks (B = -0.25, 95%CI = -0.48 to -0.02). Associations were stronger in the younger participants and for agoraphobia and GAD-7 scores also in those with type 2 diabetes.CONCLUSION: Multiple anxiety disorders and generalized anxiety symptoms were associated with worse cognitive functioning on several cognitive domains.

Purpose The Quality of Life Alzheimer’s Disease Scale (QoL-AD) is commonly used to assess disease specific health-related quality of life (HRQoL) as rated by patients and their carers. For cost-effectiveness analyses, utilities based on the EQ-5D are often required. We report a new mapping algorithm to obtain EQ-5D indices when only QoL-AD data are available. Methods Different statistical models to estimate utility directly, or responses to individual EQ-5D questions (response mapping) from QoL-AD, were trialled for patient-rated and proxy-rated questionnaires. Model performance was assessed by root mean square error and mean absolute error. Results The response model using multinomial regression including age and sex, performed best in both the estimation dataset and an independent dataset. Conclusions The recommended mapping algorithm allows researchers for the first time to estimate EQ-5D values from QoL-AD data, enabling cost-utility analyses using datasets where the QoL-AD but no utility measures were collected.

Additional file 1. Participant characteristics.

Additional file 4. Contrast enhancement in the CVOs and normal-appearing brain matter.

BackgroundThis study is part of the Research to Assess Policies and Strategies for Dementia in the Young project. Information about specific needs in young onset dementia (YOD) will provide the basis for the development of an e-health intervention to assist caregivers in coping with YOD in several European countries. ObjectiveThe aim was to investigate the issues caregivers of people with YOD face. MethodsA qualitative content analysis method was used to analyse interviews with YOD caregivers. Quantitative data of the Needs in Young Onset Dementia study were used to select caregivers based on a ranking of unmet needs, to capture differences and similarities between caregivers that experienced high levels of unmet needs versus those with low levels of unmet needs. Needs were assessed with the Camberwell Assessment of Needs in the Elderly. ResultsFindings revealed the following themes: (i) acceptance; (ii) perception of the relationship; (iii) role adaptation; (iv) Availability of appropriate services; (v) social support; and (vi) awareness in the person with dementia and acceptance of help. Several factors seemed more apparent in the caregivers who experienced few unmet needs opposed to the caregivers who experienced more unmet needs. ConclusionThe current study provides an in-depth perspective on the caregiver's experiences and emphasizes specific themes that could be addressed in future interventions. This might contribute to a caring situation in which the caregiver experiences less unmet needs. Copyright (c) 2017 John Wiley & Sons, Ltd.

ObjectiveModifiable risk factors for dementia were recently identified and compiled in a systematic review. The Lifestyle for Brain Health' (LIBRA) score, reflecting someone's potential for dementia prevention, was studied in a large longitudinal population-based sample with respect to predicting cognitive change over an observation period of up to 16years. MethodsLifestyle for Brain Health was calculated at baseline for 949 participants aged 50-81years from the Maastricht Ageing Study. The predictive value of LIBRA for incident dementia and cognitive impairment was examined by using Cox proportional hazard models and by testing its relation with cognitive decline. ResultsLifestyle for Brain Health predicted future risk of dementia, as well as risk of cognitive impairment. A one-point increase in LIBRA score related to 19% higher risk for dementia and 9% higher risk for cognitive impairment. LIBRA predicted rate of decline in processing speed, but not memory or executive functioning. ConclusionsLifestyle for Brain Health (LIBRA) may help in identifying and monitoring risk status in dementia-prevention programmes, by targeting modifiable, lifestyle-related risk factors.

This is a protocol for a Cochrane Review (Diagnostic test accuracy). The objectives are as follows: Our primary objective is to determine the diagnostic accuracy of the APOE‐ε4 allele in detecting AD dementia and other types of dementia in people with MCI in a primary care setting.

BackgroundDepression is a highly prevalent disease with a high morbidity and mortality risk. Its pathophysiology is not entirely clear. However, type 2 diabetes is an important risk factor for depression. One mechanism that may explain this association may include the formation of advanced glycation end products (AGEs). We therefore investigated the association of AGEs with depressive symptoms and depressive disorder. In addition, we examined whether the potential association was present for somatic and/or cognitive symptoms of depression. MethodsCross-sectional data were used from the Maastricht Study (N = 862, mean age 59.8 8.5 years, 55% men). AGE accumulation was measured with skin autofluorescence (SAF) by use of the AGE Reader. Plasma levels of protein-bound pentosidine were measured with high-performance liquid chromatography and fluorescence detection. N epsilon-(carboxymethyl)lysine (CML) and N epsilon-(carboxyethyl)lysine (CEL) were measured with ultraperformance liquid chromatography and tandem mass spectrometry. Depressive symptoms and depressive disorder were assessed by the nine-item Patient Health Questionnaire and the Mini-International Neuropsychiatric Interview. ResultsHigher SAF was associated with depressive symptoms ( = 0.42, 95% CI 0.12-0.73, P = .007) and depressive disorder (OR = 1.42, 95% CI 1.04-1.95, P = .028) after adjustment for age, sex, type 2 diabetes, smoking, BMI, and kidney function. Plasma pentosidine, CML, and CEL were not independently associated with depressive symptoms and depressive disorder. ConclusionsThis study shows that AGE accumulation in the skin is independently associated with higher levels of depressive symptoms and depressive disorder. This association is present for both somatic and cognitive symptoms of depression. This might suggest that AGEs are involved in the development of depression.

Introduction: \ud \ud The Stroke and Cognition consortium (STROKOG) aims to facilitate a better understanding of the determinants of vascular contributions to cognitive disorders and help improve the diagnosis and treatment of vascular cognitive disorders (VCD).\ud \ud Methods: \ud \ud Longitudinal studies with ≥75 participants who had suffered or were at risk of stroke or TIA and which evaluated cognitive function were invited to join STROKOG. The consortium will facilitate projects investigating rates and patterns of cognitive decline, risk factors for VCD, and biomarkers of vascular dementia.\ud \ud Results: \ud \ud Currently, STROKOG includes 25 (21 published) studies, with 12,092 participants from five continents. The duration of follow-up ranges from 3 months to 21 years.\ud \ud Discussion: \ud \ud Although data harmonization will be a key challenge, STROKOG is in a unique position to reuse and combine international cohort data and fully explore patient level characteristics and outcomes. STROKOG could potentially transform our understanding of VCD and have a worldwide impact on promoting better vascular cognitive outcomes.

Copyright © 2015 American Medical Association. All rights reserved., Importance: Cerebral amyloid-β aggregation is an early pathological event in Alzheimer disease (AD), starting decades before dementia onset. Estimates of the prevalence of amyloid pathology in persons without dementia are needed to understand the development of AD and to design prevention studies. Objective: To use individual participant data meta-analysis to estimate the prevalence of amyloid pathology as measured with biomarkers in participants with normal cognition, subjective cognitive impairment (SCI), or mild cognitive impairment (MCI). Data sources: Relevant biomarker studies identified by searching studies published before April 2015 using the MEDLINE and Web of Science databases and through personal communication with investigators. Study selection: Studies were included if they provided individual participant data for participants without dementia and used an a priori defined cutoff for amyloid positivity. Data extraction and synthesis: Individual records were provided for 2914 participants with normal cognition, 697 with SCI, and 3972 with MCI aged 18 to 100 years from 55 studies. Main outcomes and measures: Prevalence of amyloid pathology on positron emission tomography or in cerebrospinal fluid according to AD risk factors (age, apolipoprotein E [APOE] genotype, sex, and education) estimated by generalized estimating equations. Results: The prevalence of amyloid pathology increased from age 50 to 90 years from 10% (95% CI, 8%-13%) to 44% (95% CI, 37%-51%) among participants with normal cognition; from 12% (95% CI, 8%-18%) to 43% (95% CI, 32%-55%) among patients with SCI; and from 27% (95% CI, 23%-32%) to 71% (95% CI, 66%-76%) among patients with MCI. APOE-ε4 carriers had 2 to 3 times higher prevalence estimates than noncarriers. The age at which 15% of the participants with normal cognition were amyloid positive was approximately 40 years for APOE ε4ε4 carriers, 50 years for ε2ε4 carriers, 55 years for ε3ε4 carriers, 65 years for ε3ε3 carriers, and 95 years for ε2ε3 carriers. Amyloid positivity was more common in highly educated participants but not associated with sex or biomarker modality. Conclusions and relevance: Among persons without dementia, the prevalence of cerebral amyloid pathology as determined by positron emission tomography or cerebrospinal fluid findings was associated with age, APOE genotype, and presence of cognitive impairment. These findings suggest a 20- to 30-year interval between first development of amyloid positivity and onset of dementia., The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement No. 115372, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in-kind contribution. BIOMARKAPD is an EU Joint Programme–Neurodegenerative Disease Research (JPND) project. The project is supported through national funding organizations under the aegis of JPND (http://www.jpnd.eu). In the Netherlands, this is ZonMw. The DESCRIPA study was funded by the European Commission within the 5th framework program (QLRT-2001-2455). The EDAR study was funded by the European Commission as part of the 6th framework programme (contract No. 37670). This research was performed within the framework of the Center for Translational Molecular Medicine (CTTM) (http://www.ctmm.nl), project LeARN (grant 02N-101). The AIBL study was funded in part by the study partners (Australian Commonwealth Scientific Industrial and Research Organization [CSIRO], Edith Cowan University [ECU], Mental Health Research Institute [MHRI], Alzheimer’s Australia [AA], National Ageing Research Institute [NARI], Austin Health, CogState, Hollywood Private Hospital, Sir Charles Gardner Hospital). The study also received support from the National Health and Medical Research Council (NHMRC) and the Dementia Collaborative Research Centres program (DCRC2), as well as ongoing funding from the Science and Industry Endowment Fund (SIEF). Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health grant U01 AG024904) and the US Department of Defense ADNI (W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; BioClinica; Biogen Idec; Bristol-Myers Squibb Company; Eisai; Elan Pharmaceuticals; Eli Lilly; F. Hoffmann-La Roche and its affiliated company Genentech; GE Healthcare; Innogenetics; IXICO; Janssen Alzheimer Immunotherapy Research & Development; Johnson & Johnson Pharmaceutical Research & Development; Medpace; Merck; Meso Scale Diagnostics; NeuroRx Research; Novartis Pharmaceuticals; Pfizer; Piramal Imaging; Servier; Synarc; and Takeda Pharmaceutical. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (http://www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. The Dementia Competence Network (DCN) has been supported by a grant from the German Federal Ministry of Education and Research (BMBF): Kompetenznetz Demenzen (01GI0420). Additional funding related to the randomized clinical trials came from Janssen-Cilag and Merz Pharmaceuticals. The latter funds were exclusively used for personnel, pharmaceuticals, blistering and shipment of medication, and monitoring and as capitation fees for recruiting centers. Funding source for the Chandigarh study is the Indian Council of Medical Research (ICMR), India. Funding for the St Louis contribution was provided by the National Institute on Aging (P50 AG005681, P01 AG003991, and P01 AG026276); Fred Simmons and Olga Mohan, and the Charles and Joanne Knight Alzheimer’s Research Initiative of the Washington University Knight Alzheimer’s Disease Research Center. The Tours study received financial support of the French Ministry of Health grant PHRC-N 2008 1004 and the EC-FP6-project DiMI, LSHB-CT-2005-512146. The Caen study was funded by Agence Nationale de la Recherche, Programme Hospitalier de Recherche Clinique, Région Basse Normandie, and Institut National de la Santé et de la Recherche Médicale (Inserm). The research leading to the Munich contribution to the Mattsson multicenter study has received funding from the program “Investissements d’avenir” (ANR-10-IAIHU-06). The study from Pittsburgh was supported by National Institutes of Health grants (P50 AG005133, R37 AG025516, P01 AG025204). The New York contributions to the Mattsson multicenter study were in part supported by P30 AG008051, R01 AG13616, R01 AG022374, and R01 AG12101. Data from Brescia in this article were collected by Translational Outpatient Memory Clinic (TOMC) working group at IRCCS Fatebenefratelli in Brescia, Italy. Contributors to the TOMC are G. Amicucci, S. Archetti, L. Benussi, G. Binetti, L. Bocchio-Chiavetto, C. Bonvicini, E. Canu, F. Caobelli, E. Cavedo, E. Chittò, M. Cotelli, M. Gennarelli, S. Galluzzi, C. Geroldi, R. Ghidoni, R. Giubbini, U. P. Guerra, G. Kuffenschin, G. Lussignoli, D. Moretti, B. Paghera, M. Parapini, C. Porteri, M. Romano, S. Rosini, I. Villa, R. Zanardini, and O. Zanetti. The JPND Project is supported in Italy by the Italian Ministry of Health. The assembling of the TU Munich data set was supported in part by the German research foundation (Deutsche Forschungsgemeinschaft) (HE 4560/1-2, DR 445/3-1 and DR 445/4-1 to A.D.), and by a KKF grant for clinical research of the Technische Universität München (to A.D. and T.G.). The Florbetaben phase 2 study from which data were derived for this multicenter evaluation was sponsored by Bayer Healthcare/Piramal Imaging (Berlin, Germany). This work was supported by the University of Antwerp Research Fund; the Alzheimer Research Foundation (SAO-FRA); the Research Foundation Flanders (FWO); the Agency for Innovation by Science and Technology (IWT); the Belgian Science Policy Office Interuniversity Attraction Poles (IAP) program; and the Flemish Government–initiated Methusalem excellence grant.