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Samantha Burnham | Research Statistician 17 June 2013 CSIRO MATHEMATICS INFORMATICS AND STATISTICS | PREVENTATIVE HEALTH AIBL: A blood-based predictor for neocortical Aβ burden

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Samantha Burnham | Research Statistician17 June 2013

CSIRO MATHEMATICS INFORMATICS AND STATISTICS | PREVENTATIVE HEALTH

AIBL: A blood-based predictor for neocortical Aβ burden

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Bioinformatics and Biostatistics | Sam Burnham

Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade, Clifford R Jack Jr, David S Knopman, et al. Lancet Neurol 2010; 9: 119–28

The Hypothesis

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Bioinformatics and Biostatistics | Sam Burnham

Motivation

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Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer's disease: a prospective cohort study, V.L. Villemagne, S.C. Burnham, et al. Lancet Neurol 2013; 12 (4): 357-67

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Bioinformatics and Biostatistics | Sam Burnham

Cohorts

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Bioinformatics and Biostatistics | Sam Burnham

Methods

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NAB

Demographics

Blood Measures

=• SUVR• + vs -

• Age• Gender• ...

• Blood 1• Blood 2• ...• Blood 176

Cross Validate

Validate

• Base Model

• Full model

Check Value Added

Random Forest

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Bioinformatics and Biostatistics | Sam Burnham

ResultsModel Markers AIBL Imaged Cohort

Sens% (SD%)

Spec% (SD%)

AUC%(SD%)

M1 Age, APOE Genotype, CXCL-13, IgM-1, IL-17, PPY, VCAM-1, Aβ1-42, CDR sum of boxes

79.6 (1.3)

82.4 (1.2)

87.6 (0.7)

M2 Age, APOE Genotype, CXCL-13, IgM-1, IL-17, PPY, VCAM-

1, Aβ1-42

79.6 (1.6)

79.4 (1.4)

83.9 (1.0)

M3 Age, CXCL-13, IgM-1, IL-17,

PPY, VCAM-1, Aβ1-42

73.2(1.8)

73.3(1.8)

80.8 (1.7)

M4 Age, APOE Genotype, CDR sum of boxes

71.4 (1.1)

71.5 (1.1)

78.3 (0.8)

M5 Age, CDR sum of boxes 69.0 (2.1)

68.9 (2.2)

76.0 (2.0)

M6 Age, APOE Genotype 66.7 (1.6)

66.7 (1.6)

70.2 (1.3)

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A blood-based predictor for neocortical Aβ burden in Alzheimer’s disease: results from the AIBL study, S.C. Burnham, N.G. Faux, et al. Mol Psy 2013; doi:10.1038/mp.2013.40

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Bioinformatics and Biostatistics | Sam Burnham

ValidationModel Markers ADNI Imaged

CohortSens

% Spec

% AUC

%

M1 Age, APOE Genotype, CXCL-13, IgM-1, IL-17, PPY, VCAM-1, Aβ1-

42, CDR sum of boxes78.3 76.3 84.7

M2 Age, APOE Genotype, CXCL-13,

IgM-1, IL-17, PPY, VCAM-1, Aβ1-4273.9 74.6 81.7

M3 Age, CXCL-13, IgM-1, IL-17, PPY,

VCAM-1, Aβ1-4268.1 73.6 68.8

M4 Age, APOE Genotype, CDR sum of boxes 72.7 67.9 73.7

M5 Age, CDR sum of boxes 59.1 62.3 64.5M6 Age, APOE Genotype 77.3 62.3 67.8

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A blood-based predictor for neocortical Aβ burden in Alzheimer’s disease: results from the AIBL study, S.C. Burnham, N.G. Faux, et al. Mol Psy 2013; doi:10.1038/mp.2013.40

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Bioinformatics and Biostatistics | Sam Burnham

Other Support

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A blood-based predictor for neocortical Aβ burden in Alzheimer’s disease: results from the AIBL study, S.C. Burnham, N.G. Faux, et al. Mol Psy 2013; doi:10.1038/mp.2013.40

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Bioinformatics and Biostatistics | Sam Burnham

Summary

• Early detection is critical

• Likely need multimodal and multistage screening process

• Require cheap and non-invasive medium for widespread screen

• Proof of principal for blood biomarker use

• Composite scores, unlikely to be a single marker

• Validation is imperative

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Bioinformatics and Biostatistics | Sam Burnham

Acknowledgments

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AIBL is a large collaborative study and a complete list of contributors can be found at www.aibl.csiro.au AIBL is funded in part by a grant from the Science and Industry Endowment Fund. We thank all who took part in the study.

Acknowledgements and thanks to the AIBL study participants and their families

David AmesArti AppannahMary BarnesKevin BarnhamJustin BedoShayne BellinghamLynette BonPierrick BourgeatBelinda BrownRachel BuckleySamantha BurnhamAshley BushGraeme ChandlerPrastishtha ChatterjeeKarren ChenRoger ClarnetteSteven CollinsIan CookeTiffany CowieKay CoxRhona CreeganEmily CuninghamDavid DarbyKarl DeruyckPatricia DesmondHang DingJames DoeckeVincent DoreHarriet DowningBelinda DridanKonsta DuesingDenise El- SheikhKathryn Ellis Michael FaheyMaree FarrowNoel FauxMichael Fenech

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