Modifiable Risk Factors and Prevention of Dementia

Yaffe, Kristine

doi:10.1001/jamainternmed.2017.7299

Cited by 75 publications

(53 citation statements)

References 107 publications

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“…However, longitudinal studies showed that up to 50% of patients with MCI at their first doctor visit returned to normal at follow-up examinations (Larrieu et al, 2002;Ganguli et al, 2004), particularly in population-based cohorts. Apart from neurodegenerative disorders, many factors affect cognitive performance in older populations, including age, gender, education, vascular risk factors, genetic background, hormonal changes, and comorbidities (Yaffe, 2018). Additionally, studies of MCI differed greatly as to their inclusion criteria, study settings, sample characteristics, classification and subtype of MCI, observation period, and evaluation of cognitive and other relevant features.…”

Section: Introductionmentioning

confidence: 99%

Depression, Anxiety, and Apathy in Mild Cognitive Impairment: Current Perspectives

2020

Front. Aging Neurosci.

164

127

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Objective: Mild cognitive impairment (MCI) is an important risk state for dementia, particularly Alzheimer's disease (AD). Depression, anxiety, and apathy are commonly observed neuropsychiatric features in MCI, which have been linked to cognitive and functional decline in daily activities, as well as disease progression. Accordingly, the study's objective is to review the prevalence, neuropsychological characteristics, and conversion rates to dementia between MCI patients with and without depression, anxiety, and apathy.Methods: A PubMed search and critical review were performed relating to studies of MCI, depression, anxiety, and apathy.Results: MCI patients have a high prevalence of depression/anxiety/apathy; furthermore, patients with MCI and concomitant depression/anxiety/apathy have more pronounced cognitive deficits and progress more often to dementia than MCI patients without depression/anxiety/apathy. Conclusions and Implications: Depression, anxiety, and apathy are common in MCI and represent possible risk factors for cognitive decline and progression to dementia. Further studies are needed to better understand the role and neurobiology of depression, anxiety, and apathy in MCI.

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Section: Introductionmentioning

confidence: 99%

Depression, Anxiety, and Apathy in Mild Cognitive Impairment: Current Perspectives

2020

Front. Aging Neurosci.

164

127

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“…Although the impact of these factors varies at different life stages, eliminating them at any stage would be beneficial. Moreover, studies recommend active treatment and intervention of modifiable dementia risk factors, which would potentially delay or prevent 30% of dementia cases [1], [4].…”

Section: A Dementia Risk Factorsmentioning

confidence: 99%

“…www.ijacsa.thesai.org Risk factors for dementia can be either modifiable or nonmodifiable [3]. Fortunately, dementia could be delayed or possibly prevented by eliminating some of its modifiable risk factors [4].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach towards Detecting Dementia based on its Modifiable Risk Factors

Bin-Hezam¹,

Ward²

2019

IJACSA

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Dementia is considered one of the greatest global health and social care challenges in the 21st century. Fortunately, dementia can be delayed or possibly prevented by changes in lifestyle as dictated through known modifiable risk factors. These risk factors include low education, hypertension, obesity, hearing loss, depression, diabetes, physical inactivity, smoking, and social isolation. Other risk factors are non-modifiable and include aging and genetics. The main goal of this study is to demonstrate how machine learning methods can help predict dementia based on an individual's modifiable risk factors profile. We use publicly available datasets for training algorithms to predict participant' s cognitive state diagnosis, as cognitive normal or mild cognitive impairment or dementia. Several approaches were implemented using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) longitudinal study. The best classification results were obtained using both the Lancet and the Libra risk factor lists via longitudinal datasets, which outperformed cross-sectional baseline datasets. Moreover, using only data of the most recent visits provided even better results than using the complete longitudinal set. A binary classification (dementia vs. nondementia) yielded approximately 92% accuracy, while the full multi-class prediction performance yielded to a 77% accuracy using logistic regression, followed by random forest with 92% and 70% respectively. The results demonstrate the utility of machine learning in the prediction of cognitive impairment based on modifiable risk factors and may encourage interventions to reduce the prevalence or severity of the condition in large populations.

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“…A steep increase in the prevalence of dementia is expected [1], associated with social, economic, and societal challenges. Early detection of dementia is crucial for an implementation of therapeutic strategies in the earliest disease stages [2], and reliable cognitive screening tools play an important role in this process of identifying individuals with cognitive impairment [3]. In the context of clinical research, accurate cognitive assessment tools are needed for an adequate selection of participants, since erroneous inclusion or exclusion of individuals may bias study findings [4,5].…”

Section: Introductionmentioning

confidence: 99%

Enhanced diagnostic accuracy for neurocognitive disorders: a revised cut-off approach for the Montreal Cognitive Assessment

et al. 2020

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Background: The Montreal Cognitive Assessment (MoCA) has good sensitivity for mild cognitive impairment, but specificity is low when the original cutoff (25/26) is used. We aim to revise the cutoff on the German MoCA for its use in clinical routine. Methods: Data were analyzed from 496 Memory Clinic outpatients (447 individuals with a neurocognitive disorder; 49 with cognitive normal findings) and from 283 normal controls. Cutoffs were identified based on (a) Youden's index and (b) the 10th percentile of the control group. Results: A cutoff of 23/24 on the MoCA had better correct classification rates than the MMSE and the original MoCA cutoff. Compared to the original MoCA cutoff , the cutoff of 23/24 points had higher specificity (92% vs 63%), but lower sensitivity (65% vs 86%). Introducing two separate cutoffs increased diagnostic accuracies with 92% specificity (23/24 points) and 91% sensitivity (26/27 points). Scores between these two cutoffs require further examinations. Conclusions: Using two separate cutoffs for the MoCA combined with scores in an indecisive area enhances the accuracy of cognitive screening.

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Modifiable Risk Factors and Prevention of Dementia

Abstract: In 2017, the International Diabetes Federation (IDF) estimated that 451 million people were diabetic and will exceed 690 million by 2045. 12

Cited by 75 publications

References 107 publications

Depression, Anxiety, and Apathy in Mild Cognitive Impairment: Current Perspectives

Depression, Anxiety, and Apathy in Mild Cognitive Impairment: Current Perspectives

A Machine Learning Approach towards Detecting Dementia based on its Modifiable Risk Factors

Enhanced diagnostic accuracy for neurocognitive disorders: a revised cut-off approach for the Montreal Cognitive Assessment

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