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)