Household-level lifestyle interventions for the prevention of cognitive decline; A Systematic review

Costello, Maria; McCarthy, Christine; Judge, Conor; Dennehy, Karen; Dermott, Clodagh Mc; Flatharta, Tomás Ó; O’Donnell, Martin J.; Canavan, Michelle

doi:10.1016/j.archger.2021.104565

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…Dementia, a progressive neurodegenerative disorder, is closely linked with several risk factors [ 5 ], including those with modifiable properties [ 6 ]. Various modifiable risk factors have been identified as contributing to dementia development, such as cardiometabolic risk, physical activity [ 7 , 8 ], depressive symptoms [ 9 ], use of psychotropic drugs [ 10 , 11 ], medications with anticholinergic burden [ 12 , 13 ], and Helicobacter pylori infection [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Using Hypothesis-Led Machine Learning and Hierarchical Cluster Analysis to Identify Disease Pathways Prior to Dementia: Longitudinal Cohort Study

Huang,

Hsiao,

Tsai

et al. 2023

J Med Internet Res

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Background Dementia development is a complex process in which the occurrence and sequential relationships of different diseases or conditions may construct specific patterns leading to incident dementia. Objective This study aimed to identify patterns of disease or symptom clusters and their sequences prior to incident dementia using a novel approach incorporating machine learning methods. Methods Using Taiwan’s National Health Insurance Research Database, data from 15,700 older people with dementia and 15,700 nondementia controls matched on age, sex, and index year (n=10,466, 67% for the training data set and n=5234, 33% for the testing data set) were retrieved for analysis. Using machine learning methods to capture specific hierarchical disease triplet clusters prior to dementia, we designed a study algorithm with four steps: (1) data preprocessing, (2) disease or symptom pathway selection, (3) model construction and optimization, and (4) data visualization. Results Among 15,700 identified older people with dementia, 10,466 and 5234 subjects were randomly assigned to the training and testing data sets, and 6215 hierarchical disease triplet clusters with positive correlations with dementia onset were identified. We subsequently generated 19,438 features to construct prediction models, and the model with the best performance was support vector machine (SVM) with the by-group LASSO (least absolute shrinkage and selection operator) regression method (total corresponding features=2513; accuracy=0.615; sensitivity=0.607; specificity=0.622; positive predictive value=0.612; negative predictive value=0.619; area under the curve=0.639). In total, this study captured 49 hierarchical disease triplet clusters related to dementia development, and the most characteristic patterns leading to incident dementia started with cardiovascular conditions (mainly hypertension), cerebrovascular disease, mobility disorders, or infections, followed by neuropsychiatric conditions. Conclusions Dementia development in the real world is an intricate process involving various diseases or conditions, their co-occurrence, and sequential relationships. Using a machine learning approach, we identified 49 hierarchical disease triplet clusters with leading roles (cardio- or cerebrovascular disease) and supporting roles (mental conditions, locomotion difficulties, infections, and nonspecific neurological conditions) in dementia development. Further studies using data from other countries are needed to validate the prediction algorithms for dementia development, allowing the development of comprehensive strategies to prevent or care for dementia in the real world.

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Section: Introductionmentioning

confidence: 99%

Using Hypothesis-Led Machine Learning and Hierarchical Cluster Analysis to Identify Disease Pathways Prior to Dementia: Longitudinal Cohort Study

Huang,

Hsiao,

Tsai

et al. 2023

J Med Internet Res

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Factors Associated with Mild Cognitive Impairment in Community-Dwelling Elderly Individuals: A Re-evaluation of Systematic Reviews

Cao,

Wang,

Xue

et al. 2024

WJPH

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Objective This study aims to re-evaluate systematic reviews on factors associated with mild cognitive impairment (MCI) in community-dwelling elderly individuals to inform prevention and intervention strategies in community settings. Methods Comprehensive searches were conducted across multiple databases, including CNKI, Wanfang Data, VIP Journal Integration Platform, PubMed, Scopus, Web of Science, and Cochrane Library, to identify systematic reviews on factors contributing to MCI in community-dwelling elderly individuals. Dual reviewers screened the literature, and the methodological quality was assessed using A Measurement Tool to Assess Systematic Reviews (AMSTAR 2). Relevant factors were summarized and analyzed. Results Eleven systematic reviews were included in the analysis. Of these, two were classified as high quality, two as low quality, and the remaining seven as very low quality. Protective factors against MCI included the consumption of tea, fish, and shellfish, physical exercise, and social participation. Risk factors encompassed a history of chronic diseases, depression, sleep disorders, smoking, and alcohol consumption. Conclusion The methodological quality of systematic reviews on factors associated with MCI in community-dwelling elderly individuals is generally low. MCI is closely linked to various aspects of physiological health, psychological health, dietary nutrition, and lifestyle behaviors. It is crucial to focus on high-risk groups, particularly the elderly and females, as well as individuals experiencing subjective cognitive decline, and conduct psychological screenings and assessments of dietary quality. These factors may serve as early indicators for MCI in community-dwelling elderly individuals.

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Using Hypothesis-Led Machine Learning and Hierarchical Cluster Analysis to Identify Disease Pathways Prior to Dementia: Longitudinal Cohort Study (Preprint)

Huang¹,

Tsai²,

Chen³

et al. 2022

Preprint

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BACKGROUND Dementia development is a complex process in which the occurrence and sequential relationships of different diseases or conditions may construct specific patterns leading to incident dementia. OBJECTIVE This study aimed to identify patterns of disease or symptom clusters and their sequences prior to incident dementia using a novel approach incorporating machine learning methods to identify at-risk patterns of disease or symptom clusters and their sequences for preventive intervention activities. METHODS Using Taiwan’s National Health Insurance Research Database (NHIRD), data from 15,700 older people with dementia and 15,700 nondementia controls matched on age, sex, and index year (training dataset [67%] and the testing dataset [33%]) were retrieved for analysis. Using machine learning methods to capture specific hierarchical disease triplet clusters prior to dementia, we designed a study algorithm with four steps: (1) data preprocessing, (2) disease pathway selection, (3) model construction and optimization, and (4) data visualization. RESULTS Among 15,700 identified older people with dementia, 10,466 and 5,234 subjects were randomly assigned to the training and testing datasets, and 6,215 hierarchical disease triplet clusters with positive correlations with dementia onset were identified. We subsequently generated 19,438 features to construct prediction models, and the model with the best performance was support vector machine (SVM) with the by-group Lasso regression method (total corresponding features=2,513; accuracy=0.615; sensitivity=0.607; specificity=0.622; positive prediction value [PPV]=0.612; negative prediction value [NPV]=0.619; area under the curve [AUC]=0.639). In total, the current study captured 49 hierarchical disease triplet clusters related to dementia development, and the most characteristic patterns leading to incident dementia started with cardiovascular conditions (mainly hypertension), cerebrovascular disease, mobility disorders, or infections, followed by neuropsychiatric conditions. CONCLUSIONS Dementia development in the real world is an intricate process involving various diseases or conditions, their co-occurrence, and sequential relationships. Using a machine learning approach, we identified 49 hierarchical disease triplet clusters with leading roles (cardio- or cerebrovascular disease) and supporting roles (mental conditions, locomotion difficulties, infections, and nonspecific neurological conditions) in dementia development. Further studies using data from other countries are needed to validate the prediction algorithms for dementia development, allowing the development of comprehensive strategies to prevent or care for dementia in the real world.

show abstract

Household-level lifestyle interventions for the prevention of cognitive decline; A Systematic review

Cited by 3 publications

References 29 publications

Using Hypothesis-Led Machine Learning and Hierarchical Cluster Analysis to Identify Disease Pathways Prior to Dementia: Longitudinal Cohort Study

Using Hypothesis-Led Machine Learning and Hierarchical Cluster Analysis to Identify Disease Pathways Prior to Dementia: Longitudinal Cohort Study

Factors Associated with Mild Cognitive Impairment in Community-Dwelling Elderly Individuals: A Re-evaluation of Systematic Reviews

Using Hypothesis-Led Machine Learning and Hierarchical Cluster Analysis to Identify Disease Pathways Prior to Dementia: Longitudinal Cohort Study (Preprint)

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