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Wang T.,Shanghai JiaoTong University | Xiao S.,Shanghai JiaoTong University | Chen K.,Banner Alzheimers Institute And Banner Good Samaritan Pet Center | Yang C.,Shanghai JiaoTong University | And 11 more authors.
Current Alzheimer Research | Year: 2017

Background: Amnestic MCI (aMCI) has notably increased in Shanghai, China. Objective: The study was designed to estimate the prevalence and incidence rates of aMCI and to determine the risk and protective factors for aMCI among persons ≥ 60 years-old and ≥ 70 years-old in Shanghai communities, respectively. Method: We carried out this 1-year longitudinal study to survey a random sample of 1,302 individuals ≥ 60 years-old, to collect baseline and follow-up data about lifestyle through self-reports, and vascular and comorbid conditions from medical records and a physical examination. We also analyzed a subgroup of individuals ≥ 70 years-old. Results: The prevalence rate of aMCI in persons ≥ 60 years-old was 22.3%, and the incidence rate (per 1,000 person-years) was 96.9. Being female was a risk factor for aMCI; protective factors included smoking, drinking tea, engaging in intellectual work before retirement, social activities and hobbies, regular reading habits, and surfing the internet. The prevalence rate of aMCI in persons ≥ 70 years was 30.3%, and the incidence rate was 145.6. Smoking, drinking tea, and surfing the internet were not protective factors for this age group (≥ 70 years). Conclusion: The present study indicates that aMCI is a considerable health problem in Shanghai. Preventive strategies for aMCI are needed to enhance lifestyle factors that promote brain activity. © 2017 Bentham Science Publishers.


Liu K.,Beijing Normal University | Chen K.,Banner Alzheimers Institute And Banner Good Samaritan Pet Center | Yao L.,Beijing Normal University | Guo X.,Beijing Normal University
Frontiers in Human Neuroscience | Year: 2017

Mild cognitive impairment (MCI) represents a transitional stage from normal aging to Alzheimer’s disease (AD) and corresponds to a higher risk of developing AD. Thus, it is necessary to explore and predict the onset of AD in MCI stage. In this study, we propose a combination of independent component analysis (ICA) and the multivariate Cox proportional hazards regression model to investigate promising risk factors associated with MCI conversion among 126 MCI converters and 108 MCI non-converters from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Using structural magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) data, we extracted brain networks from AD and normal control groups via ICA and then constructed Cox models that included network-based neuroimaging factors for the MCI group. We carried out five separate Cox analyses and the two-modality neuroimaging Cox model identified three significant network-based risk factors with higher prediction performance (accuracy = 73.50%) than those in either single-modality model (accuracy = 68.80%). Additionally, the results of the comprehensive Cox model, including significant neuroimaging factors and clinical variables, demonstrated that MCI individuals with reduced gray matter volume in a temporal lobe-related network of structural MRI [hazard ratio (HR) = 8.29E-05 (95% confidence interval (CI), 5.10E-07 ∼ 0.013)], low glucose metabolism in the posterior default mode network based on FDG-PET [HR = 0.066 (95% CI, 4.63E-03 ∼ 0.928)], positive apolipoprotein E ε4-status [HR = 1. 988 (95% CI, 1.531 ∼ 2.581)], increased Alzheimer’s Disease Assessment Scale-Cognitive Subscale scores [HR = 1.100 (95% CI, 1.059 ∼ 1.144)] and Sum of Boxes of Clinical Dementia Rating scores [HR = 1.622 (95% CI, 1.364 ∼ 1.930)] were more likely to convert to AD within 36 months after baselines. These significant risk factors in such comprehensive Cox model had the best prediction ability (accuracy = 84.62%, sensitivity = 86.51%, specificity = 82.41%) compared to either neuroimaging factors or clinical variables alone. These results suggested that a combination of ICA and Cox model analyses could be used successfully in survival analysis and provide a network-based perspective of MCI progression or AD-related studies. © 2017 Liu, Chen, Yao and Guo.

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