Longitudinal analysis of risk factors for dementia based on Mild Cognitive Impairment Screen results and questionnaire responses from healthy Japanese individuals registered in an online database

Ogawa, Masayo; Maruo, Kazushi; Sone, Daichi; Shimada, Hiroyuki; Suzuki, Keisuke; Watanabe, Hiroshi; Mizusawa, Hidehiro

doi:10.1016/j.trci.2019.06.003

“…The project is part of the GR2021 Priority project Healthy Brains for Life (age 20–99 years) and focuses on the decentralized and remote assessment of the symptoms of preclinical stages in Alzheimer's disease and movement disorders, e.g ., Parkinson's, with a rationale and a methodology similar to other international initiatives. Relevant examples of similar large-scale national initiatives can be found in Japan with the IROOP registry system for identifying risk factors for dementia 16 , the Sydney (Australia) memory and ageing study 17 , the Framingham heart study in the USA 18 , the UK Biobank study of lifestyle and genetic factors incidence in dementia 19 , the European Prevention of Alzheimer’s Dementia Longitudinal Cohort Study 20 , the FINGER project in Finland 21 , the INTERCEPTOR Project in Italy 22 or The Vallecas Project in Spain 23 .…”

Section: Introductionmentioning

confidence: 99%

Can detection and prediction models for Alzheimer’s Disease be applied to Prodromal Parkinson’s Disease using explainable artificial intelligence? A brief report on Digital Neuro Signatures.

Tarnanas¹,

Vlamos²,

Harms³

2022

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Parkinson's disease (PD) is the fastest growing neurodegeneration and has a prediagnostic phase with a lot of challenges to identify clinical and laboratory biomarkers for those in the earliest stages or those 'at risk'. Despite the current research effort, further progress in this field hinges on the more effective application of digital biomarker and artificial intelligence applications at the prediagnostic stages of PD. It is of the highest importance to stratify such prediagnostic subjects that seem to have the most neuroprotective benefit from drugs. However, current initiatives to identify individuals at risk or in the earliest stages that might be candidates for future clinical trials are still challenging due to the limited accuracy and explainability of existing prediagnostic detection and progression prediction solutions. In this brief paper, we report on a novel digital neuro signature (DNS) for prodromal-PD based on selected digital biomarkers previously discovered on preclinical Alzheimer's disease. (AD). Our preliminary results demonstrated a standard DNS signature for both preclinical AD and prodromal PD, containing a ranked selection of features. This novel DNS signature was rapidly repurposed out of 793 digital biomarker features and selected the top 20 digital biomarkers that are predictive and could detect both the biological signature of preclinical AD and the biological mechanism of a-synucleinopathy in prodromal PD. The resulting model can provide physicians with a pool of patients potentially eligible for therapy and comes along with information about the importance of the digital biomarkers that are predictive, based on SHapley Additive exPlanations (SHAP). Similar initiatives could clarify the stage before and around diagnosis, enabling the field to push into unchartered territory at the earliest stages of the disease.

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“…The project is part of the GR2021 Priority project Healthy Brains for Life (age 20-99 years) and focuses on the decentralized and remote assessment of the symptoms of preclinical stages in Alzheimer's disease and movement disorders, e.g., Parkinson's, with a rationale and a methodology similar to other international initiatives. Relevant examples of similar large-scale national initiatives can be found in Japan with the IROOP registry system for identifying risk factors for dementia 16 , the Sydney (Australia) memory and ageing study 17 , the Framingham heart study in the USA 18 , the UK Biobank study of lifestyle and genetic factors incidence in dementia 19 , the European Prevention of Alzheimer's Dementia Longitudinal Cohort Study 20 , the FINGER project in Finland 21 , the INTERCEPTOR Project in Italy 22 or The Vallecas Project in Spain 23 .…”

Section: Introductionmentioning

confidence: 99%

Can detection and prediction models for Alzheimer’s Disease be applied to Prodromal Parkinson’s Disease using explainable artificial intelligence? A brief report on Digital Neuro Signatures.

Tarnanas¹,

Vlamos²

2021

Open Res Europe

0

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Parkinson's disease (PD) is the fastest growing neurodegeneration and has a prediagnostic phase with a lot of challenges to identify clinical and laboratory biomarkers for those in the earliest stages or those 'at risk'. Despite the current research effort, further progress in this field hinges on the more effective application of digital biomarker and artificial intelligence applications at the prediagnostic stages of PD. It is of the highest importance to stratify such prediagnostic subjects that seem to have the most neuroprotective benefit from drugs. However, current initiatives to identify individuals at risk or in the earliest stages that might be candidates for future clinical trials are still challenging due to the limited accuracy and explainability of existing prediagnostic detection and progression prediction solutions. In this brief paper, we report on a novel digital neuro signature (DNS) for prodromal-PD based on selected digital biomarkers previously discovered on preclinical Alzheimer's disease. (AD). Our preliminary results demonstrated a standard DNS signature for both preclinical AD and prodromal PD, containing a ranked selection of features. This novel DNS signature was rapidly repurposed out of 793 digital biomarker features and selected the top 20 digital biomarkers that are predictive and could detect both the biological signature of preclinical AD and the biological mechanism of a-synucleinopathy in prodromal PD. The resulting model can provide physicians with a pool of patients potentially eligible for therapy and comes along with information about the importance of the digital biomarkers that are predictive, based on SHapley Additive exPlanations (SHAP). Similar initiatives could clarify the stage before and around diagnosis, enabling the field to push into unchartered territory at the earliest stages of the disease.

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“…The project focuses on early detection of cognitive impairment and Alzheimer’s type dementia with a rationale and a methodology similar to other international initiatives. Relevant examples of comparable large-scale national initiatives can be found in Japan with the IROOP registry system for identifying risk factors for dementia 38 , the Sidney (Australia) memory and ageing study 39 , the Framingham heart study in the US 40 , the UK Biobank study of lifestyle and genetic factors incidence in dementia 41 , the European Prevention of Alzheimer’s Dementia Longitudinal Cohort Study 42 , the FINGER project in Finland 43 or the INTERCEPTOR Project in Italy 44 .…”

Section: Introductionmentioning

confidence: 99%

Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods

Gómez-Ramírez

¹

,

Ávila-Villanueva

²

,

Fernández-Blázquez

³

2020

Sci Rep

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Alzheimer’s Disease is a complex, multifactorial, and comorbid condition. The asymptomatic behavior in the early stages makes the identification of the disease onset particularly challenging. Mild cognitive impairment (MCI) is an intermediary stage between the expected decline of normal aging and the pathological decline associated with dementia. The identification of risk factors for MCI is thus sorely needed. Self-reported personal information such as age, education, income level, sleep, diet, physical exercise, etc. is called to play a key role not only in the early identification of MCI but also in the design of personalized interventions and the promotion of patients empowerment. In this study, we leverage a large longitudinal study on healthy aging in Spain, to identify the most important self-reported features for future conversion to MCI. Using machine learning (random forest) and permutation-based methods we select the set of most important self-reported variables for MCI conversion which includes among others, subjective cognitive decline, educational level, working experience, social life, and diet. Subjective cognitive decline stands as the most important feature for future conversion to MCI across different feature selection techniques.

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Longitudinal analysis of risk factors for dementia based on Mild Cognitive Impairment Screen results and questionnaire responses from healthy Japanese individuals registered in an online database

Cited by 3 publications

References 24 publications

Can detection and prediction models for Alzheimer’s Disease be applied to Prodromal Parkinson’s Disease using explainable artificial intelligence? A brief report on Digital Neuro Signatures.

Can detection and prediction models for Alzheimer’s Disease be applied to Prodromal Parkinson’s Disease using explainable artificial intelligence? A brief report on Digital Neuro Signatures.

Can detection and prediction models for Alzheimer’s Disease be applied to Prodromal Parkinson’s Disease using explainable artificial intelligence? A brief report on Digital Neuro Signatures.

Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods

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