Blood-based protein biomarkers for diagnosis of Alzheimer disease

Abstract

Objective: To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD).

Design: Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data.

Setting: General community-based, prospective, longitudinal study of aging.

Participants: A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort.

Results: A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, β(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD. Cross-validated accuracy measures from the AIBL cohort reached a mean (SD) of 85% (3.0%) for sensitivity and specificity and 93% (3.0) for the area under the receiver operating characteristic curve. A second validation using the ADNI cohort attained accuracy measures of 80% (3.0%) for sensitivity and specificity and 85% (3.0) for area under the receiver operating characteristic curve.

Conclusions: This study identified a panel of plasma biomarkers that distinguish individuals with AD from cognitively healthy control subjects with high sensitivity and specificity. Cross-validation within the AIBL cohort and further validation within the ADNI cohort provides strong evidence that the identified biomarkers are important for AD diagnosis.

Figure 1.

Statistical method mind-map. Defines commonly used terms for statistical analyses pathways 1 and 2, statistical methods sets 1 and 2, and model prediction approaches 1, 2, and 3. APOE indicates apolipoprotein E; LIMMA, linear models for microarray; SAM, significance analyses of microarray; and SVM, support vector machine.

Figure 2.

Biomarker Venn diagram. This diagram defines the numbers of protein biomarkers selected by each of the 2 statistical methods sets (set 1/set 2) and support vector machine (SVM) analyses. Overlap between 3 circles defines the number of markers chosen by all 3 groups; overlap between 2 circles defines the number of markers that were chosen by the 2 overlapping circle groups. The numbers in the black circles define how the initial 21-biomarker list was achieved.

Figure 3.

Receiver operating characteristic (ROC) curves for comparison of biomarker set performance in the Australian Imaging Biomarker and Lifestyle study (AIBL) and the Alzheimer Disease Neuroimaging Initiative (ADNI) data sets. A, The ROC curves represent the area under the ROC curve (AUC) as tested using the AIBL data only. The AUC comparison in the AIBL data set used (1) age, sex, and presence of the apolipoprotein E (APOE) ε4 allele (red line); (2) age, sex, APOE ε4 allele, and the 18-variable biomarker set A (green line); and (3) age, sex, APOE ε4 allele, and the reduced 8-variable biomarker set B (blue line). B, The ROC curves represent a comparison between class predictions when using only AIBL protein biomarker data or when using ADNI protein biomarkers as the validatory data set. The AUC comparison of 4 different prediction models used (1) the AIBL data set: age, sex, presence of the APOE ε4 allele, and biomarker set A (red line; equivalent of green line in A); (2) cross validation of the AIBL data set using markers available from both cohorts, that is, age, sex, presence of the APOE ε4 allele, and biomarker set A minus interleukin 17 (IL-17), Zn, and homocysteine (blue line); (3) the ADNI data set: age, sex, presence of the APOE ε4 allele, and biomarker set A minus IL-17, Zn, and homocysteine (green line); and (4) the ADNI data set: age, sex, APOE ε4 allele, and biomarker set B minus IL-17 (yellow line).