scholarly journals Correction: Correlation between cognition and plasma noradrenaline level in Alzheimer’s disease: a potential new blood marker of disease evolution

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Laure-Elise Pillet ◽  
Camille Taccola ◽  
Justine Cotoni ◽  
Hervé Thiriez ◽  
Karine André ◽  
...  

A Correction to this paper has been published: https://doi.org/10.1038/s41398-020-01102-y

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Laure-Elise Pillet ◽  
Camille Taccola ◽  
Justine Cotoni ◽  
Hervé Thiriez ◽  
Karine André ◽  
...  

AbstractRecent evidence showing degeneration of the noradrenergic system in the locus coeruleus (LC) in Alzheimer’s disease (AD) has motivated great interest in noradrenaline (NA) as a potential brain hallmark of the disease. Despite the current exploration of blood markers for AD, the deregulation of the plasma NA concentration ([NA]plasma) in AD is currently not well understood. This retrospective study includes a cohort of 71 patients (32 AD patients, 22 with other dementia and 17 without dementia) who were given consultations for memory complaints in the Cognitive Neurology Center of Lariboisière (Paris) between 2009 and 2014. As previously described in brain tissue, we show for the first time a linear correlation between [NA]plasma and Mini Mental State Examination (MMSE) score in AD patients. We observed that high [NA]plasma in AD patients was associated with higher [Aβ1–42]CSF than in other AD patients with [NA]plasma similar to NC patients. In parallel, we observed a lower (p-Tau/Tau)CSF in AD patients with low [NA]plasma than in non-AD patients with [NA]plasma similar to [NA]plasma in NC patients. Our data suggest that [NA]plasma could be a potential biomarker of disease evolution in the context of AD and could possibly improve early diagnosis.


2020 ◽  
Vol 16 (S4) ◽  
Author(s):  
Patrick Oeckl ◽  
Steffen Halbgebauer ◽  
Sarah Straub ◽  
Christine von Arnim ◽  
Janine Diehl‐Schmid ◽  
...  

2020 ◽  
Vol 4 (2) ◽  
pp. 397-415
Author(s):  
Emma Muñoz-Moreno ◽  
Raúl Tudela ◽  
Xavier López-Gil ◽  
Guadalupe Soria

The research of Alzheimer’s disease (AD) in its early stages and its progression till symptomatic onset is essential to understand the pathology and investigate new treatments. Animal models provide a helpful approach to this research, since they allow for controlled follow-up during the disease evolution. In this work, transgenic TgF344-AD rats were longitudinally evaluated starting at 6 months of age. Every 3 months, cognitive abilities were assessed by a memory-related task and magnetic resonance imaging (MRI) was acquired. Structural and functional brain networks were estimated and characterized by graph metrics to identify differences between the groups in connectivity, its evolution with age, and its influence on cognition. Structural networks of transgenic animals were altered since the earliest stage. Likewise, aging significantly affected network metrics in TgF344-AD, but not in the control group. In addition, while the structural brain network influenced cognitive outcome in transgenic animals, functional network impacted how control subjects performed. TgF344-AD brain network alterations were present from very early stages, difficult to identify in clinical research. Likewise, the characterization of aging in these animals, involving structural network reorganization and its effects on cognition, opens a window to evaluate new treatments for the disease.


2020 ◽  
Author(s):  
Clément Abi Nader ◽  
Nicholas Ayache ◽  
Giovanni B. Frisoni ◽  
Philippe Robert ◽  
Marco Lorenzi ◽  
...  

AbstractRecent failures of clinical trials in Alzheimer’s Disease underline the critical importance of identifying optimal intervention time to maximize cognitive benefit. While several models of disease progression have been proposed, we still lack quantitative approaches simulating the effect of treatment strategies on the clinical evolution. In this work, we present a data-driven method to model dynamical relationships between imaging and clinical biomarkers. Our approach allows simulating intervention at any stage of the pathology by modulating the progression speed of the biomarkers, and by subsequently assessing the impact on disease evolution. When applied to multi-modal imaging and clinical data from the Alzheimer’s Disease Neuroimaging Initiative our method enables to generate hypothetical scenarios of amyloid lowering interventions. Our results show that in a study with 1000 individuals per arm, accumulation should be completely arrested at least 5 years before Alzheimer’s dementia diagnosis to lead to statistically powered improvement of clinical endpoints.


1995 ◽  
Vol 7 (1) ◽  
pp. 115-122 ◽  
Author(s):  
Eric Jacques Souêtre ◽  
Wen Qing ◽  
Isabel Vigoureux ◽  
Jean-François Dartigues ◽  
Helene Lozet ◽  
...  

To assess the economic burden of Alzheimer's disease (AD), we carried out a cross-sectional prevalence cost-of-illness study in France. Fifty-one probable AD patients (NINCDS-ADRDA) actually treated in ambulatory care were recruited in two university outpatient centers. Demographic, clinical (including actual Mini-Mental State Examination scores), and economic data were collected by clinical investigators and trained interviewers. Total costs included actual expenditures such as direct medical costs and direct nonmedical costs, as well as indirect costs (loss of earnings due to loss of productivity). Cost valuation was based on the societal perspective using an opportunity costing approach. We found that indirect costs represented a significant portion of total costs (36%-40%). In terms of expenditures, patients and caregivers were found to bear the major part of AD total costs. We found a positive and significant correlation between disease severity and costs. Our findings support the hypothesis of a relationship between disease evolution and healthcare costs.


2014 ◽  
Vol 51 (3) ◽  
pp. 1206-1220 ◽  
Author(s):  
C. Vergara ◽  
L. Ordóñez-Gutiérrez ◽  
F. Wandosell ◽  
I. Ferrer ◽  
J. A. del Río ◽  
...  

1987 ◽  
Vol 76 (1) ◽  
pp. 79-86
Author(s):  
Osamu MOHARA ◽  
Yuji UENO ◽  
Hideki NISHIO ◽  
Yoshinari NAKAMURA ◽  
Yoshiaki MASUYAMA

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