scholarly journals Genetic Interaction with Plasma Lipids on Alzheimer’s Disease in the Framingham Heart Study

2018 ◽  
Vol 66 (3) ◽  
pp. 1275-1282
Author(s):  
Gina M. Peloso ◽  
Alexa S. Beiser ◽  
Anita L. Destefano ◽  
Sudha Seshadri
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Framingham Heart Study ◽  
Plasma Lipids ◽  
Genetic Interaction ◽  
Heart Study

scholarly journals Inverse association between cancer and Alzheimer's disease: results from the Framingham Heart Study

BMJ ◽  
2012 ◽  
Vol 344 (mar12 1) ◽  
pp. e1442-e1442 ◽  
Author(s):  
J. A. Driver ◽  
A. Beiser ◽  
R. Au ◽  
B. E. Kreger ◽  
G. L. Splansky ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Framingham Heart Study ◽  
Inverse Association ◽  
Heart Study

scholarly journals Severity distribution of Alzheimer’s disease dementia and mild cognitive impairment in the Framingham Heart Study

2020 ◽  
Author(s):  
Jing Yuan ◽  
Nancy Maserejian ◽  
Yulin Liu ◽  
Sherral Devine ◽  
Cai Gillis ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Framingham Heart Study ◽  
Time Windows ◽  
Population Based ◽  
Clinical Syndrome ◽  
Mild Disease ◽  
Heart Study

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.

Associations of loneliness with risk of Alzheimer's disease dementia in the Framingham Heart Study

2021 ◽  
Author(s):  
Samia C. Akhter‐Khan ◽  
Qiushan Tao ◽  
Ting Fang Alvin Ang ◽  
Indira Swetha Itchapurapu ◽  
Michael L. Alosco ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Framingham Heart Study ◽  
Heart Study ◽  
Alzheimer’S Disease Dementia

O4-02-01: Plasma clusterin levels and risk of dementia and Alzheimer's disease: The Framingham Heart Study

2013 ◽  
Vol 9 ◽  
pp. P681-P681
Author(s):  
Galit Weinstein ◽  
Alexa Beiser ◽  
Paul Courchesne ◽  
Vincent Chouraki ◽  
Rhoda Au ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Framingham Heart Study ◽  
Heart Study

scholarly journals Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification

Brain ◽  
2020 ◽  
Vol 143 (6) ◽  
pp. 1920-1933 ◽  
Author(s):  
Shangran Qiu ◽  
Prajakta S Joshi ◽  
Matthew I Miller ◽  
Chonghua Xue ◽  
Xiao Zhou ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Framingham Heart Study ◽  
Disease Risk ◽  
Imaging Techniques ◽  
Disease Diagnosis ◽  
Disease Classification ◽  
Imaging Biomarker ◽  
Heart Study

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.

Association between widespread pain and dementia, Alzheimer’s disease and stroke: a cohort study from the Framingham Heart Study

2021 ◽  
pp. rapm-2021-102733
Author(s):  
Kanran Wang ◽  
Hong Liu
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Chronic Pain ◽  
Cohort Study ◽  
Framingham Heart Study ◽  
Widespread Pain ◽  
Dementia Risk ◽  
Heart Study ◽  
Alzheimer’S Disease Dementia

Background and objectiveChronic pain may be an early indicator of cognitive decline, but previous studies have not systematically examined the population-level associations between widespread pain and adverse cognitive outcomes and stroke. This study was designed to determine the association between widespread pain, a common subtype of chronic pain, and subsequent dementia, Alzheimer’s disease dementia and stroke.MethodsThis retrospective cohort study used data from the US community-based Framingham Heart Study. Pain status was assessed at a single time point between 1990 and 1994. Widespread pain was determined based on the Framingham Heart Study pain homunculus. Dementia follow-up occurred across a median of 10 years (IQR, 6–13 years) for persons who were dementia free at baseline. Proportional hazard models examined associations between widespread pain and incident dementia, Alzheimer’s disease dementia and stroke.ResultsA total of 347 (14.1%) subjects fulfilled the criteria for widespread pain, whereas 2117 (85.9%) subjects did not. Of 188 cases of incident all-cause dementia, 128 were Alzheimer’s disease dementia. In addition, 139 patients suffered stroke during the follow-up period. After multivariate adjustment including age and sex, widespread pain was associated with 43% increase in all-cause dementia risk (HR: 1.43; 95% CI 1.06 to 1.92), 47% increase in Alzheimer’s disease dementia risk (HR: 1.47; 95% CI 1.13 to 2.20) and 29% increase in stroke risk (HR: 1.29; 95% CI 1.08 to 2.54). Comparable results were shown in the subgroup of individuals over 65 years old.ConclusionWidespread pain was associated with an increased incidence of all-cause dementia, Alzheimer’s disease dementia and stroke.Trial registration numberNCT00005121.

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