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Dementia Risk Prediction Development of the first tool for LMICs (WS2)

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Presentation on theme: "Dementia Risk Prediction Development of the first tool for LMICs (WS2)"— Presentation transcript:

1 Dementia Risk Prediction Development of the first tool for LMICs (WS2)
WS2.1 Leads Dr Blossom Stephan, Dr Matthew Prina and Dr Devi Mohan

2 Overview Background Current state of knowledge
Dementia risk prediction in Low and Middle Income Countries (LMICs) WS2.1 Work plan What is needed from the group Expected outcomes

3 Background Dementia is incurable
Why is dementia risk prediction important? Dementia is incurable Maybe difficult to reverse neuropathological changes once symptoms are diagnosable Focus on pre-symptomatic and at-risk cases

4 Current State of Knowledge
Dementia Risk Prediction Modelling No method is recommended for assessing future risk of dementia in clinical or population settings Systematic review (updated 20152) Numerous models (>50) Area under the curve (AUC) range 0.50 to 0.81 Only developed for predicting Alzheimer’s disease or all-cause dementia Few models have been externally validated Mixed results 1 Stephan et al., Nature Reviews Neurology 2010; 2 Tang et al., PLoS One, 2015

5 Comparison of 3 Risk Models
CAIDE1 LLDRI2 ANU-ADRI3 N = 1,409 (Mage = 50) Follow-up = 20 years Outcome = Dementia Country = Finland N = 3,375 (Mage = 76) Follow-up = 6 years Country = USA Evidence Synthesis (Review) Outcome = AD 1 Kivipelto et al., Lan Neurl, 2006; 2 Barnes et al, Neurology, 2009; 3 Anstey et al., Prev Science, 2013; Anstey et al., PLoS One 2014

6 Comparison of 3 Risk Models
CAIDE1 LLDRI2 ANU-ADRI3 N=1,409 (Mage= 50) Follow-up = 20 years Outcome = Dementia Country = Finland N=3,375 (Mage= 76) Follow-up = 6 years Country = USA Evidence Synthesis (Review) Outcome = AD Age Body Mass Index (BMI) BMI 1 Kivipelto et al., Lan Neurl, 2006; 2 Barnes et al, Neurology, 2009; 3 Anstey et al., Prev Science, 2013; Anstey et al., PLoS One 2014

7 Comparison of 3 Risk Models
CAIDE1 LLDRI2 ANU-ADRI3 N=1,409 (Mage= 50) Follow-up = 20 years Outcome = Dementia Country = Finland N=3,375 (Mage= 76) Follow-up = 6 years Country = USA Evidence Synthesis (Review) Outcome = AD Education Cholesterol Physical Activity Alcohol 1 Kivipelto et al., Lan Neurl, 2006; 2 Barnes et al, Neurology, 2009; 3 Anstey et al., Prev Science, 2013; Anstey et al., PLoS One 2014

8 Comparison of 3 Risk Models
CAIDE1 LLDRI2 ANU-ADRI3 N=1,409 (Mage= 50) Follow-up = 20 years Outcome = Dementia Country = Finland N=3,375 (Mage= 76) Follow-up = 6 years Country = USA Evidence Synthesis (Review) Outcome = AD Age BMI Education Digit Symbol Substitution Test Cholesterol Mini Mental State Examination Physical Activity Carotid Artery Thickening Sex Alcohol Systolic BP History of Bypass Surgery Diabetes Slow Physical Performance Traumatic Brain Injury Genetics (APOE) Smoking MRI White Matter Disease Cognitive Activity MRI Ventricular Enlargement Social Engagement Depression Fish Intake Pesticide Exposure Cardiovascular Risk Factors, Aging and Dementia (CAIDE) study 1 Kivipelto et al., Lan Neurl, 2006; 2 Barnes et al, Neurology, 2009; 3 Anstey et al., Prev Science, 2013; Anstey et al., PLoS One 2014

9 Comparison of 3 Risk Models
CAIDE1 LLDRI2 ANU-ADRI3 N=1,409 (Mage= 50) Follow-up = 20 years Outcome = Dementia Country = Finland N=3,375 (Mage= 76) Follow-up = 6 years Country = USA Evidence Synthesis (Review) Outcome = AD Age Age (by sex groups) BMI Education DSST Cholesterol MMSE Physical Activity Carotid Artery thickening Sex Alcohol Systolic BP History of Bypass Surgery Diabetes Slow Physical Performance Traumatic Brain Injury Genetics (APOE) Smoking MRI White Matter Disease Cognitive Activity MRI Ventricular Enlargement Social Engagement Depression Fish Intake Pesticide Exposure AUC = 0.77 (0.71 to 0.83) AUC = 0.81 (0.79 to 0.83) AUC Range = 0.64 to 0.73 1 Kivipelto et al., Lan Neurl, 2006; 2 Barnes et al, Neurology, 2009; 3 Anstey et al., Prev Science, 2013; Anstey et al., PLoS One 2014

10 Dementia Risk Prediction in LMICs
Ethnic disparities in dementia prevalence and risk factors No risk model developed OR tested for use in LMICs One model1 developed in Mexican Americans Included Age, sex, education, not having friends to count on, not attending community events, diabetes, feeling the blues, pain, impairment in instrumental activities of daily living and unable to walk a half-mile Area under the curve 0.74 (95% CI=0.70–0.78) for 10-year incident dementia Reference 1 Downer et al, JAGS, 2016

11 WS2.1: Aim To develop simple models, for non specialists to use, to identify people at high risk of future dementia in each partner country

12 WS2.1: Work-plan Undertake a narrative literature review to identify candidate risk factors unique to each partner country (i.e. language, Ethnicity) Country specific reports & discussion yesterday Based on the systematic reviews of previous risk models and results from Step 1 undertake new model development using country specific data Regression analysis with competing risk (i.e. death) Using data from the 10/66 study undertake model external validation (i.e. transportability)

13 WS2.1: What is Needed from the Group
Data resources Access to cohort or clinical data with longitudinal follow-up and dementia as an outcome Possible resources for risk analysis and prediction South East Asian Community Observatory Malaysian Elders Longitudinal Research Studies, MELoR Health Action for People Hai District Demographic Surveillance Site 10/66 Study Others(?) Queries Where will the analysis be run? If in the UK, how to attain data access/transfer

14 WS2.1: Expected Outcomes Timeline Months 1 to 20 Four key outcomes:
Updated literature search A dementia risk assessment model that is accuracy, valid and feasible for use in each partner LMICs Possibility for App development Translation

15 WS2.1: Work to date Completed:
Country specific reports with risk factors identified Literature update on new developments in dementia risk prediction model development and testing (Eleni and Eugene) To do: Identify data sources (for 6 month review) Request data from each partner Complete statistical analysis Prepare manuscript(s)


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