Importance The association between peripheral inflammatory biomarkers and Alzheimer disease (AD) is not consistent in the literature. It is possible that chronic inflammation, rather than 1 episode of inflammation, interacts with genetic vulnerability to increase the risk for AD. Objective To study the interaction between the apolipoprotein E ( ApoE ) genotype and chronic low-grade inflammation and its association with the incidence of AD. Design, Setting, and Participants In this cohort study, data from 2656 members of the Framingham Heart Study offspring cohort (Generation 2; August 13, 1971-November 27, 2017) were evaluated, including longitudinal measures of serum C-reactive protein (CRP), diagnoses of incident dementia including AD, and brain volume. Chronic low-grade inflammation was defined as having CRP at a high cutoff level at a minimum of 2 time points. Statistical analysis was performed from December 1, 1979, to December 31, 2015. Main Outcomes and Measures Development of AD and brain volumes. Results Of the 3130 eligible participants, 2656 (84.9%; 1227 men and 1429 women; mean [SD] age at last CRP measurement, 61.6 [9.5] years) with both ApoE status and longitudinal CRP measurements were included in this study analysis. Median (interquartile range) CRP levels increased with mean (SD) age (43.3 [9.6] years, 0.95 mg/L [0.40-2.35 mg/L] vs 59.1 [9.6] years, 2.04 mg/L [0.93-4.75 mg/L] vs 61.6 [9.5] years, 2.21 mg/L [1.05-5.12 mg/L]; P < .001), but less so among those with ApoE4 alleles, followed by ApoE3 then ApoE2 genotypes. During the 17 years of follow-up, 194 individuals (7.3%) developed dementia, 152 (78.4%) of whom had AD. ApoE4 coupled with chronic low-grade inflammation, defined as a CRP level of 8 mg/L or higher, was associated with an increased risk of AD, especially in the absence of cardiovascular diseases (hazard ratio, 6.63; 95% CI, 1.80-24.50; P = .005), as well as an increased risk of earlier disease onset compared with ApoE4 carriers without chronic inflammation (hazard ratio, 3.52; 95% CI, 1.27-9.75; P = .009). This phenomenon was not observed among ApoE3 and ApoE2 carriers with chronic low-grade inflammation. Finally, a subset of 1761 individuals (66.3%) underwent brain magnetic resonance imaging, and the interaction between ApoE4 and chronic low-grade inflammation was associated with brain atrophy in the temporal lobe (β = –0.88, SE = 0.22; P < .001) and hippocampus (β = –0.04, SE = 0.01; P = .005), after adjusting for confounders. Conclusions...
Introduction:The relationship between persistent loneliness and Alzheimer's disease (AD) is unclear. We examined the relationship between different types of mid-life loneliness and the development of dementia and AD.Methods: Loneliness was assessed in cognitively normal adults using one item from the Center for Epidemiologic Studies Depression Scale. We defined loneliness as no loneliness, transient loneliness, incident loneliness,or persistent loneliness, and applied Cox regression models and Kaplan-Meier plots with dementia and AD as outcomes (n = 2880).
Ensemble 1 learning use multiple algorithms to obtain better predictive performance than any single one of its constituent algorithms could. With growing popularity of deep learning, researchers have started to ensemble them for various purposes. Few if any, however, has used the deep learning approach as a means to ensemble algorithms. This paper presents a deep ensemble learning framework which aims to harness deep learning algorithms to integrate multisource data and tap the 'wisdom of experts'. At the voting layer, a sparse autoencoder is trained for feature learning to reduce the correlation of attributes and diversify the base classifiers ultimately. At the stacking layer, a nonlinear feature-weighted method based on deep belief networks is proposed to rank the base classifiers which may violate the conditional independence. Neural network is used as meta classifier. At the optimizing layer, under-sampling and threshold-moving are used to cope with cost-sensitive problem. Optimized predictions are obtained based on ensemble of probabilistic predictions by similarity calculation. The proposed deep ensemble learning framework is used for Alzheimer's disease classification. Experiments with the clinical dataset from national Alzheimer's coordinating center demonstrate that the classification accuracy of our proposed framework is 4% better than 6 well-known ensemble approaches as well as the standard stacking algorithm. Adequate coverage of more accurate diagnostic services can be provided by utilizing the wisdom of averaged physicians. This paper points out a new way to boost the primary care of Alzheimer's disease from the view of machine learning.
Growing evidence relates Body Mass index (BMI) to poorer health outcomes; however, results across studies associating BMI and dementia are conflicting. A total of 3632 Framingham Offspring participants aged 20 to 60 years at their second health exam (1979-1982) were included in this study with 190 cases of incident dementia identified by 2017. Cox proportional hazards regression models were performed to investigate the association of BMI at each of their 8 exams as a baseline for dementia risk, and the associations between obesity and dementia across age groups. Spline models were fitted to investigate non-linear associations between BMI and dementia. Each 1 kg/m2 increase in BMI at 40-49 years was associated with higher risk of dementia, but lower risk after 70 years. Obesity at 40-49 years was associated with higher risk of dementia. Overall, the relationship between BMI and dementia risk was heterogeneous across the adult age range. Monitoring BMI at different age may mediate risk for dementia across an individual’s lifetime.
It has been over 20 years since Taiwan's implementation of its National Health Insurance (NHI) program. Under this program, the health insurance coverage rate has reached approximately 99% of the population. Despite guaranteeing the residents of Taiwan equal access regardless of socioeconomic status and background, critical problems and controversies persist, and they continue to challenge the NHI. We analyze the primary issues facing the NHI program with emphasis on financial and consumer behavioral aspects. Furthermore, we apply models from mainland China, South Korea and Singapore to discuss what Taiwan could learn from the systems employed by these countries to modify the NHI. Targeting the needs of the NHI, we have three policy recommendations: separating the NHI scheme into different target populations, strengthening the NHI referral system and regulating the access of overseas citizens to health services while in Taiwan. After two decades in existence, problems persist and there is a continuing need to improve Taiwan's NHI. Copyright © 2016 John Wiley & Sons, Ltd.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.