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Original Investigation |

Combined Plasma and Cerebrospinal Fluid Signature for the Prediction of Midterm Progression From Mild Cognitive Impairment to Alzheimer Disease

Benoit Lehallier, PhD1; Laurent Essioux, PhD2; Javier Gayan, PhD2; Roxana Alexandridis, PhD2; Tania Nikolcheva, MD, PhD3; Tony Wyss-Coray, PhD1,4; Markus Britschgi, PhD5 ; for the Alzheimer’s Disease Neuroimaging Initiative
[+] Author Affiliations
1Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California
2Translational Technologies and Bioinformatics, Roche Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann–La Roche, Ltd, Basel, Switzerland
3Roche Pharma Development, F. Hoffmann–La Roche, Ltd, Basel, Switzerland
4Center for Tissue Regeneration, Repair and Restoration, Veterans Affairs Palo Alto Health Care System, Palo Alto, California
5Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Areas, Roche Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann–La Roche, Ltd, Basel, Switzerland
JAMA Neurol. 2016;73(2):203-212. doi:10.1001/jamaneurol.2015.3135.
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Importance  A reliable method of detecting Alzheimer disease (AD) in its prodromal state is needed for patient stratification in clinical trials or for personalizing existing or potential upcoming therapies. Current cerebrospinal fluid (CSF)– or imaging-based single biomarkers for AD offer reliable identification of patients with underlying AD but insufficient prediction of the rate of AD progression.

Objective  To optimize prediction of progression from mild cognitive impairment (MCI) to AD dementia by combining information from diverse patient variables.

Design, Setting, and Participants  This cohort study from the Alzheimer Disease Neuroimaging Initiative (ADNI) enrolled 928 patients with MCI at baseline and 249 selected variables available in the ADNI data set. Variables included clinical and demographic data, cognitive scores, magnetic resonance imaging–based brain volumetric data, the apolipoprotein E (APOE) and translocase of outer mitochondrial membrane 40 homolog (TOMM40) genotypes, and analyte levels measured in the CSF and plasma. Data were collected in July 2012 and analyzed from July 1, 2012, to June 1, 2015.

Main Outcomes and Measures  Progression from MCI to AD within 1 to 6 years. To determine whether combinations of markers could predict progression from MCI to AD within 1 to 6 years, the elastic net algorithm was used in an iterative resampling of a training- and test-based variable selection and modeling approach.

Results  Among the 928 patients with MCI in the ADNI database, 94 had 224 of the required variables available for the modeling. The results showed the contributions of age, Clinical Dementia Rating Sum of Boxes composite test score, hippocampal volume, and multiple plasma and CSF factors in modeling progression to AD. A combination of apolipoprotein A-II and cortisol levels in plasma and fibroblast growth factor 4, heart-type fatty acid binding protein, calcitonin, and tumor necrosis factor–related apoptosis-inducing ligand receptor 3 (TRAIL-R3) in CSF allowed for reliable prediction of disease status 3 years from the time of sample collection (80% classification accuracy, 88% sensitivity, and 70% specificity).

Conclusions and Relevance  These study findings suggest that a combination of markers measured in plasma and CSF, distinct from β-amyloid and tau, could prove useful in predicting midterm progression from MCI to AD dementia. Such a large-scale, multivariable-based analytical approach could be applied to other similar large data sets involving AD and beyond.

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Figure 1.
Initial Analysis of the Data Available for 928 Patients With Mild Cognitive Impairment (MCI)

A, Overview of the 249 variables (listed in the Table and eTable 3 in the Supplement) is given in 8 categories. B, Receiver operating characteristic curves combine cerebrospinal fluid (CSF) β-amyloid 42 (Aβ42), t-tau, and p-tau in modeling progression from MCI to Alzheimer disease (AD) within 1 to 6 years. The areas under the curve (AUCs) (95% CI) are given for each year. Receiver operating characteristic curves for Aβ42, t-tau, and p-tau separately are available in eFigure 2 in the Supplement; the sample size available is shown in eTable 2 in the Supplement. Diagonal line indicates completely random discrimination; MRI, magnetic resonance imaging.

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Figure 2.
Associations Among 249 Variables Shown by a Circular Visualization of Correlation (CVC) Plot

Includes 928 patients with mild cognitive impairment. Most of the variables were unavailable for the 928 patients, as indicated in Figure 1A. We calculated the correlation coefficient between pairwise complete observations. Lines (edges) represent the Spearman rank correlation coefficient (r) between 2 variables (nodes). Variables were grouped in 8 categories. Category names of the variables are indicated; except for the cerebrospinal fluid (CSF) and plasma communicome categories, the 249 variables are labeled by numbers (more details on categories and variables are given in the Table and eTable 3 in the Supplement). The CVC plot was thresholded at |r| > 0.3 to display only the strongest correlations. AD indicates Alzheimer disease; MRI, magnetic resonance imaging.

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Figure 3.
Prediction of Progression From Mild Cognitive Impairment (MCI) to Alzheimer Disease (AD) Within 1 to 6 Years

A, Strategy for modeling progression from MCI to AD uses an elastic net to perform joint modeling and variable selection. We performed 1000-fold resampling and ranked variables according to their number of appearances in the elastic net model across the 1000 permutations. The mean classification accuracy rate, sensitivity, and specificity were calculated across the 1000 resampled test data sets. K indicates the number of variables; n, sample size. B, Sensitivity as a function of the specificity for models includes the 2 to 20 top selected variables for each time point of progression. The number of variables are indicated by the numbers in the lines in the plot. Gray quadrant indicates the area of the figure in which sensitivity and specificity are greater than 0.7. C, Classification accuracy rate as a function of its SD across permutations for models includes the top 2 to 20 selected variables for each time point of progression. A small SD across permutations (close to 0) demonstrates a high stability of the model. D, Heatmap of variables consistently selected across time points of progression. A total of 80 of 224 variables were selected at least once as 1 of the top 20 variables in the different models. Details of the 80 variables are available in eTable 5 in the Supplement. Only variables selected in the top 20 of at least 3 progression time points are represented in the heatmap. The importance index represents the number of times a variable was selected across the 1000 permutations. Aβ indicates β-amyloid; ApoA-II, apolipoprotein A-II; CA-19-9, cancer antigen 19-9; CDR SOB, Clinical Dementia Rating Sum of Boxes composite test score; CSF, cerebrospinal fluid; FGF-4, fibroblast growth factor 4; PAPP-A, pregnancy-associated plasma protein A; TECK, thymus-expressed chemokine; TF, transferrin; TRAIL-R3, tumor necrosis factor–related apoptosis-inducing ligand receptor 3; and vWF, von Willebrand factor.

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Figure 4.
Prediction of Progression From Mild Cognitive Impairment to Alzheimer Disease (AD) Within 3 Years

Prediction includes 7 models combining different subsets of variables. Correct classification rate of the top 20 variables was estimated on the test data set after 1000-fold resampling of the learning and test data sets. Sex and age were included in all models. APOE4 indicates apolipoprotein ε4; CSF, cerebrospinal fluid; and MRI, magnetic resonance imaging.

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