Potential sources of interference with the highly sensitive detection and quantification of alpha‐synuclein seeds by qRT‐QuIC

Parkinson’s disease (PD) is a progressive neurodegenerative disease which is histologically characterized by loss of dopaminergic neurons in the substantia nigra and deposition of aggregated alpha‐synuclein (aSyn) in the brain. The detection of aSyn in well accessible fluids has been one of the central approaches in the development of biomarkers for PD. Recently, real‐time quaking‐induced conversion (RT‐QuIC) has been successfully adapted for use with aSyn seeds. Here, we systematically analysed parameters potentially impacting the reliability of this assay by using quantitative real‐time quaking‐induced conversion (qRT‐QuIC) with in vitro‐formed aSyn seeds. Seeds diluted in cerebrospinal fluid (CSF) accelerated the seeding reaction and slightly increased the sensitivity without affecting specificity. Repeated freeze–thaw cycles decreased the apparent lag times of seeds diluted in ddH2O but did not alter the seeding activity of seeds diluted in CSF. High levels of artificial contamination with blood resulted in prolonged apparent lag times, while sensitivity and specificity were unaffected. Altogether, qRT‐QuIC with aSyn seems to be robust concerning sensitivity and specificity in our model system, but quantitative interpretation might be limited under certain conditions

Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study

BACKGROUND: Estimates of 'brain-predicted age' quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD) but has not been well explored in presymptomatic AD. Prior studies have typically modeled BAG with structural MRI, but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored. METHODS: We trained three models to predict age from FC, structural (S), or multimodal MRI (S+FC) in 390 amyloid-negative cognitively normal (CN/A-) participants (18-89 years old). In independent samples of 144 CN/A-, 154 CN/A+, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid and tau, as well as a global cognitive composite. RESULTS: All models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG was significantly reduced in CN/A+ participants compared to CN/A-. In CI participants only, elevated S-BAG and S+FC BAG were associated with more advanced AD pathology and lower cognitive performance. CONCLUSIONS: Both FC-BAG and S-BAG are elevated in CI participants. However, FC and structural MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to presymptomatic AD pathology, while S-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model improves sensitivity to healthy age differences. FUNDING: This work was supported by the National Institutes of Health (P01-AG026276, P01- AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, and U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimer's Association (SG-20-690363-DIAN)

Sex difference in evolution of cognitive decline: studies on mouse model and the Dominantly Inherited Alzheimer Network cohort

Women carry a higher burden of Alzheimer’s disease (AD) compared to men, which is not accounted entirely by differences in lifespan. To identify the mechanisms underlying this effect, we investigated sex-specific differences in the progression of familial AD in humans and in APPswe/PS1ΔE9 mice. Activity dependent protein translation and associative learning and memory deficits were examined in APPswe/PS1ΔE9 mice and wild-type mice. As a human comparator group, progression of cognitive dysfunction was assessed in mutation carriers and non-carriers from DIAN (Dominantly Inherited Alzheimer Network) cohort. Female APPswe/PS1ΔE9 mice did not show recall deficits after contextual fear conditioning until 8 months of age. Further, activity dependent protein translation and Akt1-mTOR signaling at the synapse were impaired in male but not in female mice until 8 months of age. Ovariectomized APPswe/PS1ΔE9 mice displayed recall deficits at 4 months of age and these were sustained until 8 months of age. Moreover, activity dependent protein translation was also impaired in 4 months old ovariectomized APPswe/PS1ΔE9 mice compared with sham female APPswe/PS1ΔE9 mice. Progression of memory impairment differed between men and women in the DIAN cohort as analyzed using linear mixed effects model, wherein men showed steeper cognitive decline irrespective of the age of entry in the study, while women showed significantly greater performance and slower decline in immediate recall (LOGIMEM) and delayed recall (MEMUNITS) than men. However, when the performance of men and women in several cognitive tasks (such as Wechsler’s logical memory) are compared with the estimated year from expected symptom onset (EYO) we found no significant differences between men and women. We conclude that in familial AD patients and mouse models, females are protected, and the onset of disease is delayed as long as estrogen levels are intact

Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study

Background: Estimates of 'brain-predicted age' quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD) but has not been well explored in presymptomatic AD. Prior studies have typically modeled BAG with structural MRI, but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored. Methods: We trained three models to predict age from FC, structural (S), or multimodal MRI (S+FC) in 390 amyloid-negative cognitively normal (CN/A-) participants (18-89 years old). In independent samples of 144 CN/A-, 154 CN/A+, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid and tau, as well as a global cognitive composite. Results: All models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG was significantly reduced in CN/A+ participants compared to CN/A-. In CI participants only, elevated S-BAG and S+FC BAG were associated with more advanced AD pathology and lower cognitive performance. Conclusions: Both FC-BAG and S-BAG are elevated in CI participants. However, FC and structural MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to presymptomatic AD pathology, while S-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model improves sensitivity to healthy age differences.This work was supported by the National Institutes of Health (P01-AG026276, P01- AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, and U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimer's Association (SG-20-690363-DIAN)

Plasma and cerebrospinal fluid glial fibrillary acidic protein levels in adults with Down syndrome: a longitudinal cohort studyResearch in context

Summary: Background: The diagnosis of symptomatic Alzheimer's disease is a clinical challenge in adults with Down syndrome. Blood biomarkers would be of particular clinical importance in this population. The astrocytic Glial Fibrillary Acidic Protein (GFAP) is a marker of astrogliosis associated with amyloid pathology, but its longitudinal changes, association with other biomarkers and cognitive performance have not been studied in individuals with Down syndrome. Methods: We performed a three-centre study of adults with Down syndrome, autosomal dominant Alzheimer's disease and euploid individuals enrolled in Hospital Sant Pau, Barcelona (Spain), Hospital Clinic, Barcelona (Spain) and Ludwig-Maximilians-Universität, Munich (Germany). Cerebrospinal fluid (CSF) and plasma GFAP concentrations were quantified using Simoa. A subset of participants had PET 18F-fluorodeoxyglucose, amyloid tracers and MRI measurements. Findings: This study included 997 individuals, 585 participants with Down syndrome, 61 Familial Alzheimer's disease mutation carriers and 351 euploid individuals along the Alzheimer's disease continuum, recruited between November 2008 and May 2022. Participants with Down syndrome were clinically classified at baseline as asymptomatic, prodromal Alzheimer's disease and Alzheimer's disease dementia. Plasma GFAP levels were significantly increased in prodromal and Alzheimer's disease dementia compared to asymptomatic individuals and increased in parallel to CSF Aβ changes, ten years prior to amyloid PET positivity. Plasma GFAP presented the highest diagnostic performance to discriminate symptomatic from asymptomatic groups (AUC = 0.93, 95% CI 0.9−0.95) and its concentrations were significantly higher in progressors vs non-progressors (p < 0.001), showing an increase of 19.8% (11.8–33.0) per year in participants with dementia. Finally, plasma GFAP levels were highly correlated with cortical thinning and brain amyloid pathology. Interpretation: Our findings support the utility of plasma GFAP as a biomarker of Alzheimer's disease in adults with Down syndrome, with possible applications in clinical practice and clinical trials. Funding: AC Immune, La Caixa Foundation, Instituto de Salud Carlos III, National Institute on Aging, Wellcome Trust, Jérôme Lejeune Foundation, Medical Research Council, Alzheimer's Association, National Institute for Health Research, EU Joint Programme–Neurodegenerative Disease Research, Alzheimer's Society, Deutsche Forschungsgemeinschaft, Stiftung für die Erforschung von Verhaltens, Fundación Tatiana Pérez de Guzmán el Bueno &amp; European Union's Horizon 2020 und Umwelteinflüssen auf die menschliche Gesundheit

Work-Life Boundaries and Well-Being: Does Work-to-Life Integration Impair Well-Being through Lack of Recovery?

Against the backdrop of increasingly blurred boundaries between work and nonwork, the purpose of this study was to investigate the implications of employees’ work-to-life boundary enactment for well-being. Using border/boundary theory (as reported by Ashforth, Kreiner, & Fugate (Academy of Management Review 25(3):472–491, 2000) and Clark (Human Relations 54(6):747–770, 2000)) and the effort-recovery model (as reported by Meijman & Mulder (Handbook of work and organizational psychology vol. 2 55–53, 1998)), we developed a research model that links work-to-life integration enactment to exhaustion and impaired work-life balance via lack of recovery activities (as reported by Sonnentag, Journal of Applied Psychology 88(3):518–528, 2003). The model was tested using structural equation modeling. Our sample consisted of N = 1916 employees who were recruited via an online panel service. Results showed that employees who scored high on work-to-life integration enactment reported less recovery activities and in turn were more exhausted and experienced less work-life balance. Our study contributes to the existing literature on boundary management by investigating the well-being implications of work-to-life boundary enactment and by suggesting and testing recovery as an underlying mechanism. In doing so, we link boundary enactment with existing theory of the work-life interface. Based on our review of existent research on boundary management and well-being, we disentangle previous contradictory findings. Understanding of the well-being implications of boundary enactment and underlying mechanisms can help human resource professionals and practitioners to devise and implement organizational policies and interventions that enable employees to develop boundary management strategies that are sustainable in that they do not impair employees’ well-being