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Abstract
Background: The goal of this study was to identify a clinical biomarker signature of brain amyloidosis in the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) mild cognitive impairment (MCI) cohort.
Methods: We developed a multimodal biomarker classifier for predicting brain amyloidosis using cognitive, imaging, and peripheral blood protein ADNI1 MCI data. We used CSF β-amyloid 1–42 (Aβ42) ≤192 pg/mL as proxy measure for Pittsburgh compound B (PiB)-PET standard uptake value ratio ≥1.5. We trained our classifier in the subcohort with CSF Aβ42 but no PiB-PET data and tested its performance in the subcohort with PiB-PET but no CSF Aβ42 data. We also examined the utility of our biomarker signature for predicting disease progression from MCI to Alzheimer dementia.
Results: The CSF training classifier selected Mini-Mental State Examination, Trails B, Auditory Verbal Learning Test delayed recall, education, APOE genotype, interleukin 6 receptor, clusterin, and ApoE protein, and achieved leave-one-out accuracy of 85% (area under the curve [AUC] = 0.8). The PiB testing classifier achieved an AUC of 0.72, and when classifier self-tuning was allowed, AUC = 0.74. The 36-month disease-progression classifier achieved AUC = 0.75 and accuracy = 71%.
Conclusions: Automated classifiers based on cognitive and peripheral blood protein variables can identify the presence of brain amyloidosis with a modest level of accuracy. Such methods could have implications for clinical trial design and enrollment in the near future.
Classification of evidence: This study provides Class II evidence that a classification algorithm based on cognitive, imaging, and peripheral blood protein measures identifies patients with brain amyloid on PiB-PET with moderate accuracy (sensitivity 68%, specificity 78%).
GLOSSARY
- Aβ42=
- β-amyloid 1–42;
- AD=
- Alzheimer dementia;
- ADNI1=
- Alzheimer's Disease Neuroimaging Initiative 1;
- AUC=
- area under the curve;
- AVLT=
- Auditory Verbal Learning Test;
- BDNF=
- brain-derived neurotrophic factor;
- CI=
- confidence interval;
- IL-6R=
- interleukin 6 receptor;
- IL-13=
- interleukin 13;
- LOOCV=
- leave-one-out cross-validation;
- MCI=
- mild cognitive impairment;
- MMSE=
- Mini-Mental State Examination;
- NPV=
- negative predictive value;
- PiB=
- Pittsburgh compound B;
- PPV=
- positive predictive value;
- ROC=
- receiver operating characteristic;
- ROI=
- region of interest;
- SUVR=
- standard uptake value ratio;
- SVM=
- support vector machine;
- TNF-α=
- tumor necrosis factor α
Footnotes
Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The ADNI investigators contributed to the design and implementation of ADNI and/or provided data. The ADNI investigators list is available on the Neurology® Web site at Neurology.org.
Go to Neurology.org for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the article.
Supplemental data at Neurology.org
- Received May 21, 2014.
- Accepted in final form October 15, 2014.
- © 2015 American Academy of Neurology
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