scholarly journals Risk Estimations, Risk Factors, and Genetic Variants Associated with Alzheimer's Disease in Selected Publications from the Framingham Heart Study

2012 ◽  
Vol 33 (s1) ◽  
pp. S439-S445 ◽  
Author(s):  
Galit Weinstein ◽  
Philip A. Wolf ◽  
Alexa S. Beiser ◽  
Rhoda Au ◽  
Sudha Seshadri
2018 ◽  
Vol 66 (3) ◽  
pp. 1275-1282
Author(s):  
Gina M. Peloso ◽  
Alexa S. Beiser ◽  
Anita L. Destefano ◽  
Sudha Seshadri

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A160-A161
Author(s):  
A Baril ◽  
A S Beiser ◽  
S Redline ◽  
E R McGrath ◽  
H J Aparicio ◽  
...  

Abstract Introduction Both sleep disturbances and inflammation are potential risk factors for Alzheimer’s disease (AD). However, it is unknown how inflammation and sleep interact together to influence the risk of developing AD dementia. Our objective was to evaluate whether interleukin-6 (IL-6) levels interact with sleep disturbances when predicting incident clinical AD. Methods We studied participants in the Framingham Heart Study Offspring cohort who completed in-home overnight polysomnography. Sleep characteristics were continuous and included sleep duration, wake after sleep onset (WASO), and apnea-hypopnea index (AHI). Participants were stratified into quartiles of IL-6 levels. Surveillance for incident AD dementia occurred over a mean follow-up of 13.4±5.4 years. Using Cox proportional hazards regression models, we tested the interaction of sleep measures by IL-6 quartiles on incident AD dementia. All analyses adjusted for age and sex and P<0.05 was considered significant. Results The final sample included 291 dementia-free participants at baseline (age 67.5±4.9 years, 51.6% men). Approximately one quarter of participants had obstructive sleep apnea (OSA; AHI>15) at baseline (median:6.2, Q1:2,3, Q3:14.3). We observed 33 cases of incident AD dementia during follow-up. Although no interaction was observed for either sleep duration or WASO with IL-6 levels, there was a significant interaction of AHI with IL-6 in predicting AD dementia (p=0.002). In the lowest IL-6 quartile, higher AHI was associated with an elevated risk of AD dementia (hazard ratio, 4.15 [95%CI, 1.42, 12.1], p=0.01) whereas no association between AHI and incident AD was observed in other IL-6 quartiles. Conclusion Our findings suggest that the pro-inflammatory cytokine IL-6 moderates the association between OSA and incident AD risk. The association between increasing OSA severity and incident AD was only observed in those with lower IL-6 levels, suggesting that this association might be especially apparent when no other confounding risk factors such as inflammation are present. Support The Framingham Heart Study is supported by contracts from the National Heart, Lung and Blood Institute, grants from the National Institute on Aging, and grants from the National Institute of Neurological Disorders and Stroke.


2009 ◽  
Vol 153B (4) ◽  
pp. 955-959 ◽  
Author(s):  
J.S.K. Kauwe ◽  
S. Bertelsen ◽  
K. Mayo ◽  
C. Cruchaga ◽  
R. Abraham ◽  
...  

BMJ ◽  
2012 ◽  
Vol 344 (mar12 1) ◽  
pp. e1442-e1442 ◽  
Author(s):  
J. A. Driver ◽  
A. Beiser ◽  
R. Au ◽  
B. E. Kreger ◽  
G. L. Splansky ◽  
...  

2020 ◽  
Author(s):  
Jing Yuan ◽  
Nancy Maserejian ◽  
Yulin Liu ◽  
Sherral Devine ◽  
Cai Gillis ◽  
...  

Abstract Background: Studies providing Alzheimer’s disease (AD) prevalence data have largely neglected to characterize the proportion of AD that is mild, moderate or severe. Estimates of the severity distribution along the AD continuum, including the mild cognitive impairment (MCI) stage, are important to plan research and allocate future resources, particularly resources targeted at particular stages of disease. Methods: Participants (aged 50-94) with prevalent MCI or AD dementia clinical syndrome were cross-sectionally selected from three time-windows of the population-based Framingham Heart Study in 2004-2005 (n=381), 2006-2007 (n=422), and 2008-2009 (n=389). Summary estimates of the severity distribution were achieved by pooling results across time-windows. Diagnosis and severity were assessed by consensus dementia review. MCI-progressive was determined if the participant had documented progression to AD dementia clinical syndrome using longitudinal data.Results: Among AD dementia participants, the pooled percentages were 50.4% for mild, 30.3% for moderate, and 19.3% for severe. Among all MCI and AD participants, the pooled percentages were 29.5%, 19.6%, 25.7%, and 45.2% for MCI-not-progressive, MCI-progressive, mild AD dementia, and the combined group of MCI-progressive & mild AD dementia, respectively. Distributions by age and sex were presented.Conclusions: Heterogeneity in severity of the AD population exists. That half of prevalent cases have mild disease underscores the need for research and interventions to slow decline of this burdensome disease.Limitations: First, the FHS cohort participants were almost homogenously Caucasians and residents of a single city in MA, that limits the generalization of the results. Second, although FHS is a longitudinal study, the study population over the three time-windows would not be expected to be as dynamic as that of sampling participants from different geographic areas. Lastly, the study lacked AD biomarker confirmation (e.g., amyloid, tau, neurodegeneration), which would have increased the accuracy of case ascertainment.


2021 ◽  
Author(s):  
Samia C. Akhter‐Khan ◽  
Qiushan Tao ◽  
Ting Fang Alvin Ang ◽  
Indira Swetha Itchapurapu ◽  
Michael L. Alosco ◽  
...  

2013 ◽  
Vol 9 ◽  
pp. P681-P681
Author(s):  
Galit Weinstein ◽  
Alexa Beiser ◽  
Paul Courchesne ◽  
Vincent Chouraki ◽  
Rhoda Au ◽  
...  

Brain ◽  
2020 ◽  
Vol 143 (6) ◽  
pp. 1920-1933 ◽  
Author(s):  
Shangran Qiu ◽  
Prajakta S Joshi ◽  
Matthew I Miller ◽  
Chonghua Xue ◽  
Xiao Zhou ◽  
...  

Abstract Alzheimer’s disease is the primary cause of dementia worldwide, with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods integrate patient history, neuropsychological testing and MRI to identify likely cases, yet effective practices remain variably applied and lacking in sensitivity and specificity. Here we report an interpretable deep learning strategy that delineates unique Alzheimer’s disease signatures from multimodal inputs of MRI, age, gender, and Mini-Mental State Examination score. Our framework linked a fully convolutional network, which constructs high resolution maps of disease probability from local brain structure to a multilayer perceptron and generates precise, intuitive visualization of individual Alzheimer’s disease risk en route to accurate diagnosis. The model was trained using clinically diagnosed Alzheimer’s disease and cognitively normal subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (n = 417) and validated on three independent cohorts: the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) (n = 382), the Framingham Heart Study (n = 102), and the National Alzheimer’s Coordinating Center (NACC) (n = 582). Performance of the model that used the multimodal inputs was consistent across datasets, with mean area under curve values of 0.996, 0.974, 0.876 and 0.954 for the ADNI study, AIBL, Framingham Heart Study and NACC datasets, respectively. Moreover, our approach exceeded the diagnostic performance of a multi-institutional team of practicing neurologists (n = 11), and high-risk cerebral regions predicted by the model closely tracked post-mortem histopathological findings. This framework provides a clinically adaptable strategy for using routinely available imaging techniques such as MRI to generate nuanced neuroimaging signatures for Alzheimer’s disease diagnosis, as well as a generalizable approach for linking deep learning to pathophysiological processes in human disease.


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