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Blood based biomarkers
Why do we want them? Exactly what do we want? How will we get it ?
Adapted from Sperling et al (2011) Alzheimer’s and dementia 7 280-92
Why do we want (blood based) biomarkers ?
-
1.0
0.6
0.2
1.4
1.8
Relativeopticaldensity
P<0.05
0.0 0.2 0.4 0.6 0.8 1.0
1 - Specificity
0.0
0.2
0.4
0.6
0.8
1.0
Sensitivity
ROC Curve
Complement Factor H (CFH)
n>500
Proof of a signal in blood
(also complement c4 – decreased)
Biomarkers for prediction
‘Conversion’, progression and sub-type
Companion biomarkers
Specific to target/therapy
Biomarkers for pathology
Discovery by endophenotype
Biomarkers for clinical trials
Stratification and experimental medicine
What, exactly, do we want ?
Biomarkers for AD
not needed, not possible
Correlation with cortical atrophy
Correlation with cognition (MMSE)
Correlation with speed of decline
Design matters
In man….
W/T APP/PS1
In mouse….
European Medical Information Framework
Vision: To enable and conduct novel research
into human health by utilising human health data
at an unprecedented scale
• Access to information on 40 million patients
• Cohorts with samples, imaging and clinical data :
• > 20,000 for dementia and metabolic biomarker discovery and validation
• Linkage of clinical and omics data
• Development of a secure (privacy, legal) modular platform
• Continue to build a network of data sources and relevant research
http://www.imi.europa.eu/content/emif
How will we get biomarkers ?
EMIF – a public private partnership for data-integration
EMIF-Metabolic
EMIF-AD
Data Privacy
Analytical tools
Semantic Integration
Information standards
Data access / mgmt
IMI Structure and Network
Research Topics
EMIF governance
Preventionalgorithms
Predictivescreening
Riskstratification
Call 5Call 5
Riskfactoranalysis
Patientgenerateddata
TBD
EMIF-Platform
Metabolic CNS
Maudsley BRC
Informatics team
Martina Sattlecker
Steven Kiddle
Steve Newhouse
Anbarasu Lourdasamy
Simon Furney
Gerome Breen
Rob Stewart
Mike Denis
Richard Dobson
King’s College London
Biomarkers team
Abdul Hye
Joanna Riddoch-Contreras
Rufina Leung
Andreas Guentert
Mohammed Aiyaz
Madhav Thambisetty (NIA)
Katie Lunnon
Alison Baird
Nicholas Ashton
Sarah Westwood
Latha Velayudhan
Chantal Bazenet
Angela Hodges
Simon Lovestone
AddNeuroMed
Imaging team
Eric Westman
Sebastian Muehlboeck
Sergi Costafreda
Lars-Olof Wahlund
Christian Spenger
Alan Evans
Andrew Simmons
AddNeuroMed
Clinical team
Hilkka Soininen
Bruno Vellas
Patrizia Meccocci
Magda Tsolaki
Iwona Kłoszewska
Simon Lovestone

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Boost Fertility New Invention Ups Success Rates.pdf
 

Alz forum lovestone (short)

  • 1. Blood based biomarkers Why do we want them? Exactly what do we want? How will we get it ?
  • 2. Adapted from Sperling et al (2011) Alzheimer’s and dementia 7 280-92 Why do we want (blood based) biomarkers ?
  • 3. - 1.0 0.6 0.2 1.4 1.8 Relativeopticaldensity P<0.05 0.0 0.2 0.4 0.6 0.8 1.0 1 - Specificity 0.0 0.2 0.4 0.6 0.8 1.0 Sensitivity ROC Curve Complement Factor H (CFH) n>500 Proof of a signal in blood (also complement c4 – decreased)
  • 4. Biomarkers for prediction ‘Conversion’, progression and sub-type Companion biomarkers Specific to target/therapy Biomarkers for pathology Discovery by endophenotype Biomarkers for clinical trials Stratification and experimental medicine What, exactly, do we want ? Biomarkers for AD not needed, not possible
  • 5. Correlation with cortical atrophy Correlation with cognition (MMSE) Correlation with speed of decline Design matters In man…. W/T APP/PS1 In mouse….
  • 6. European Medical Information Framework Vision: To enable and conduct novel research into human health by utilising human health data at an unprecedented scale • Access to information on 40 million patients • Cohorts with samples, imaging and clinical data : • > 20,000 for dementia and metabolic biomarker discovery and validation • Linkage of clinical and omics data • Development of a secure (privacy, legal) modular platform • Continue to build a network of data sources and relevant research http://www.imi.europa.eu/content/emif How will we get biomarkers ?
  • 7. EMIF – a public private partnership for data-integration EMIF-Metabolic EMIF-AD Data Privacy Analytical tools Semantic Integration Information standards Data access / mgmt IMI Structure and Network Research Topics EMIF governance Preventionalgorithms Predictivescreening Riskstratification Call 5Call 5 Riskfactoranalysis Patientgenerateddata TBD EMIF-Platform Metabolic CNS
  • 8. Maudsley BRC Informatics team Martina Sattlecker Steven Kiddle Steve Newhouse Anbarasu Lourdasamy Simon Furney Gerome Breen Rob Stewart Mike Denis Richard Dobson King’s College London Biomarkers team Abdul Hye Joanna Riddoch-Contreras Rufina Leung Andreas Guentert Mohammed Aiyaz Madhav Thambisetty (NIA) Katie Lunnon Alison Baird Nicholas Ashton Sarah Westwood Latha Velayudhan Chantal Bazenet Angela Hodges Simon Lovestone AddNeuroMed Imaging team Eric Westman Sebastian Muehlboeck Sergi Costafreda Lars-Olof Wahlund Christian Spenger Alan Evans Andrew Simmons AddNeuroMed Clinical team Hilkka Soininen Bruno Vellas Patrizia Meccocci Magda Tsolaki Iwona Kłoszewska Simon Lovestone