Plasma Biomarkers Associated with the Apolipoprotein E Genotype and Alzheimer Disease

Supplementary Online Content

Soares HD, Potter WZ, Pickering E, et al; for the Alzheimer’s Disease Neuroimaging Initiative and the Alzheimer’s Disease Plasma Proteomics Biomarkers Consortium Project Team. Plasma biomarkers associated with the apolipoprotein E genotype and Alzheimer disease. Arch Neurol. Published online July 16, 2012. doi:10.1001/archneurol.2012.1070

eTable 1. List of Analytes in Multiplex Panel

eTable 2. Quality Control Data for the ADNI/ISAB Plasma Proteomics Study

eTable 3. ANCOVA Results

eAppendix. Detailed Methods

This supplementary material has been provided by the authors to give readers additional information about their work.

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eTable 1. List of Analytes in Multiplex Panel

Italics indicates analytes that were below limit of detection and/or had more than 10% missing and were excluded from the analysis. Accession numbers for each of the analytes and details of the assays are listed on www.rulesbasedmedicine.com.

Analytes Analytes Analytes Analytes

Adiponectin Cystatin‐C Interleukin‐13 (IL‐13) PDGF‐BB

Adrenocorticotropic Hormone Endothelin‐1 (ET‐1) Interleukin‐15 (IL‐15) PAPP‐A

Agouti‐Related Protein (AGRP) EN‐RAGE Interleukin‐16 (IL‐16) Progesterone

Alpha‐1‐Antichymotrypsin Eotaxin‐1 Interleukin‐18 (IL‐18) Proinsulin, Intact

Alpha‐1‐Antitrypsin (AAT) Eotaxin‐3 Interleukin‐2 (IL‐2) Proinsulin, Total

Alpha‐1‐Microglobulin Epidermal Growth Factor (EGF) Interleukin‐25 (IL‐25) Prolactin (PRL)

Alpha‐2‐Macroglobulin Epidermal Growth Factor Rec Interleukin‐3 (IL‐3) Prostate‐Specific Antigen, Free

Alpha‐Fetoprotein (AFP) Epiregulin (EPR) Interleukin‐4 (IL‐4) Prostatic Acid Phosphatase

Amphiregulin (AR) ENA‐78 Interleukin‐5 (IL‐5) PARC

Angiopoietin‐2 (ANG‐2) Erythropoietin (EPO) Interleukin‐6 (IL‐6) RAGE

ACE E‐Selectin Interleukin‐6 receptor (IL‐6r) Resistin

Angiotensinogen Factor VII Interleukin‐7 (IL‐7) S100 calcium‐binding protein B

Apolipoprotein A‐I (Apo A‐I) Fas Ligand (FasL) Interleukin‐8 (IL‐8) Secretin Formatted: Spanish (Spain-Modern Sort) Apolipoprotein A‐II (Apo A‐II) FASLG Receptor (FAS) Kidney Injury Molecule‐1 Serotransferrin (Transferrin) Formatted: Spanish (Spain-Modern Sort) Apolipoprotein A‐IV (Apo A‐IV) FABP LOX‐1 Serum Amyloid P‐Component

Apolipoprotein B (Apo B) Ferritin (FRTN) Leptin SGOT

Apolipoprotein C‐I (Apo C‐I) Fetuin‐A Luteinizing Hormone (LH) Sex Hormone‐Binding Globulin Formatted: Spanish (Spain-Modern Sort) Apolipoprotein C‐III (Apo C‐III) Fibrinogen Lymphotactin Sortilin

Apolipoprotein D (Apo D) Fibroblast Growth Factor 4 M‐CSF Stem Cell Factor (SCF)

Apolipoprotein E (Apo E) Fibroblast Growth Factor basic MIP‐1 alpha SOD‐1

Apolipoprotein H (Apo H) Follicle‐Stimulating Hormone ( MIP‐1 beta I‐309

Apolipoprotein(a) (Lp(a)) Glucagon MIP‐3 alpha Tamm‐Horsfall Urine Glycopro

AXL Receptor Tyrosine Kinase GLP‐1 total MIF RANTES

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B Lymphocyte Chemoattract GST‐alpha MDC Tenascin‐C (TN‐C)

Beta‐2‐Microglobulin (B2M) G‐CSF MDA‐LDL Testosterone, Total

Betacellulin (BTC) GM‐CSF Matrix Metalloproteinase‐1 Thrombomodulin (TM)

Bone Morphogenetic Protein 6 Growth Hormone (GH) Matrix Metalloproteinase‐10 Thrombopoietin

N‐Terminal protype Brain Natriuretic Peptide (NT‐pro BNP) GRO‐alpha Matrix Metalloproteinase‐2 Thrombospondin‐1

BDNF Haptoglobin Matrix Metalloproteinase‐3 Thymus‐Expressed Chemokine

Calbindin Heat Shock Protein 60 (HSP‐60) Matrix Metalloproteinase‐7 Thyroid‐Stimulating Hormone

Calcitonin HB‐EGF Matrix Metalloproteinase‐9 Thyroxine‐Binding Globulin

Cancer Antigen 125 (CA‐125) Hepatocyte Growth Factor MMP‐9, total Tissue Factor (TF)

Cancer Antigen 19‐9 (CA‐19‐9) Immunoglobulin A (IgA) MCP‐1 TIMP‐1

CEA Immunoglobulin E (IgE) MCP‐2 TRAIL‐R3

CD 40 antigen (CD40) Immunoglobulin M (IGM) MCP‐3 TGF‐alpha

CD40 Ligand (CD40‐L) Insulin MCP‐4 TGF‐beta‐3

CD5 (CD5L) Insulin‐like Growth Factor I MIG Transthyretin (TTR)

Chemokine CC‐4 (HCC‐4) IGFBP‐2 MPIF‐1 Trefoil Factor 3 (TFF3)

Chromogranin‐A (CgA) ICAM‐1 Myeloperoxidase (MPO) Tumor Necrosis Factor alpha

Ciliary Neurotrophic Factor IFN‐gamma Myoglobin Tumor Necrosis Factor beta

Clusterin (CLU) IP‐10 Nerve Growth Factor beta TNFR2

Complement C3 (C3) Interleukin‐1 alpha (IL‐1 alpha) Nr‐CAM VCAM‐1

Complement Factor H Interleukin‐1 beta (IL‐1 beta) NGAL VEGF

CTGF IL‐1ra Osteopontin VKDPS

Cortisol (Cortisol) Interleukin‐10 (IL‐10) Pancreatic Polypeptide (PPP) Vitronectin

C‐peptide Interleukin‐11 (IL‐11) Peptide YY (PYY) von Willebrand Factor (vWF)

C‐Reactive Protein (CRP) IIL‐12p40 Placenta Growth Factor (PLGF)

Creatine Kinase‐MB (CK‐MB) IL‐12p70 PAI‐1

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Formatted: Font: Not Bold eAppendix. Detailed Methods

Participants

ADNI Cohort and Selection of plasma samples.

The ADNI study (http://adni.loni.ucle.edu) was launched in 2003 by the National Institute of Aging, the Foundation for NIH and by a group of private‐public partners as a precompetitive AD biomarker consortium that now is in its 7th year and will continue for another ~6 years. Enrollment target in the first phase of ADNI (ADNI‐1) was 800 participants: 200 HC subjects, 400 subjects with amnestic MCI and 200 subjects with mild AD although this cohort has been enlarged in the second phase of ADNI (ADNI‐2). The ADNI protocol was approved by the Human Studies Committee at the 58 institutions participating in ADNI in the United States and Canada. Written and verbal informed consents were obtained from participants at screening and enrollment. At sample collection, participants were ≥60 years of age and in good general health, having no other neurological, psychiatric, or major medical diagnoses that could contribute importantly to dementia. Further details regarding ADNI including participant selection procedures and complete study protocol have been presented elsewhere and can be found online at

http://www.alzheimers.org/clinicaltrials/fullrec.asp?PrimaryKey=208.

Plasma samples were collected at baseline and every 6 months after for the duration of the enrollment period for controls, MCI and AD. Only a subset of the entire plasma cohort was included in the multiplex analysis. All available baseline and one year plasma samples from the MCI arm were selected for analysis. Baseline (112) and matched one year (97) samples from the AD arm were selected based upon availability of concurrent CSF or PET imaging data. For the HC group, samples were selected if the subjects had concurrent CSF biomarker data. In addition, control samples were pre‐selected based upon baseline CSF Aβ42 levels. In controls, CSF Aβ42 showed a bimodal distribution and only subjects with CSF Aβ42 levels above the median value of the Aβ42 distribution were selected for analysis.

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Lab Test Methods

ApoE genotyping

ApoE genotyping was done for all subjects using EDTA blood samples. Taqman quantitative PCR assays were used for genotyping ApoE nucleotides 334 T/C and 472 C/T with an ABI 7900 real time thermocycler using DNA freshly prepared from EDTA whole blood.

Luminex xMAP panel

Plasma samples were evaluated by Myriad RBM, Inc. (Austin, TX) for levels of 190 analytes using the Human Discovery Multi‐Analyte Profile (MAP) ver 1.0 panel and a Luminex 100 platform. A full listing of the analytes is provided in supplementary Table 1. 146 of the 190 analytes met quality control (QC) criteria for subsequent statistical analysis. At Myriad RBM, the samples were thawed at room temperature (RT), vortexed, spun at 13,000g for 5 min for clarification, and 0.5 mL transferred to a master microtiter plate for xMAP analysis. Using automated pipetting, an aliquot of each sample was introduced into one of the capture microsphere multiplexes of the Human Discovery MAP. The mixtures of sample and capture microspheres were thoroughly mixed and incubated at RT for 1 hr. Multiplexed cocktails of biotinylated reporter antibodies for each multiplex were then added robotically, and after thorough mixing, were incubated for an additional hr at RT. Multiplexes were developed using an excess of streptavidin‐phycoerythrin solution which was thoroughly mixed into each multiplex and incubated for 1 hr at RT. The volume of each multiplexed reaction was reduced by vacuum filtration and then increased by dilution into matrix buffer for analysis that was performed in a Luminex 100 instrument and the resulting data stream was interpreted using proprietary data analysis software developed at RBM. For each analyte in a multiplex, calibrators and quality controls were included on each microtiter plate. Eight‐point calibrators were run in the first and last column of each plate and 3‐ level quality controls were included in duplicate. Testing results were determined first for the high, medium and low controls for each multiplex to ensure proper assay performance. Unknown values for each of the analytes localized in a specific multiplex were determined using 4 and 5 parameter, weighted and non‐weighted curve fitting algorithms included in the data analysis package. Validation details of all the assays are available from Myriad RBM (http://www.rulesbasedmedicine.com). Myriad RBM provided three levels of QC for each analyte. Coefficients of variation (CVs) were calculated across © 2012 American Medical Association. All rights reserved.

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plates for each analyte. Analytes were flagged if replicates at one or more of the quality control level repeats showed CVs >25%. There were 14 analytes that showed high CVs >25%. Although no analytes were excluded from the multivariate or univariate analysis based upon poor CV performance, high CVs suggested a lack of robustness in the current version of the assay. However, caution should be taken when interpreting results from these assays and details of the methodology, QC results, detection limits for assays and dynamic range for plasma are reported in data primer (http://adni.loni.ucla.edu/wp‐ content/uploads/2010/11/BC_Plasma_ Proteomics_Data_Primer.pdf ) and in supplemental material (supplemental Table 2)

Plasma Collection Methods

Patients were fasted overnight (approximately 8 hr) prior to collections. All collections were conducted in the morning and processed per ADNI laboratory standard operating procedures (SOPs) and all ADNI SOPs are available at http://adni.loni.ucle.edu. Whole blood was collected into 10 ml BD lavender top

K2EDTA (18 mg spray coated or 15% solution) coated vacutainers. Samples were gently mixed by inversion and spun at 3000 rpm for 15 min at RT within one hr of collection. Immediately after centrifugation, plasma was transferred to a polypropylene transfer tube, placed in dry ice and allowed to completely freeze. All samples were shipped in dry ice to the ADNI biomarker core laboratory at the University of Pennsylvania where they were thawed and aliquoted into 0.5 ml aliquots, transferred into polypropylene tubes and stored frozen at ‐800 C until analyzed. One of the 0.5 ml aliquots from each of the subjects mentioned above was shipped to RBM for interrogation and analysis.

Statistical Methods

For purposes of the statistical analysis, analytes that had more than 10% missing values or values listed as LOW (below the lowest assay limit defined by RBM as a value below the lowest calibrator for the individual assay) were excluded from the analysis. For analytes that had >90% of data available, values that were reported as LOW were imputed by taking the reported least detectable dose (LDD) for that individual analyte and dividing by 2. Distributions of data for individual analytes were checked for normality using Box‐Cox methods, log transformed and checked again for normality. For purposes of

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the univariate analysis, multi‐dimensional scaling and Mahalanobis distances were used to detect outliers. Outliers were defined as 5 standard deviations beyond the mean and were imputed to the nearest value within 95% of the confidence interval. Positive false discovery (pFDR) corrections were applied to p‐values to account for the multiple comparisons1. Analysis of variance (ANOVA) and analysis of covariance (ANCOVA) models including diagnosis, age, gender and ApoE4 allelic status were utilized to compare mean analyte levels across groups. Group comparisons were made between AD versus controls and MCI versus controls at baseline and at 12 months based upon diagnosis at time of blood draw. Preliminary group comparisons were also made between 1) MCI progressors (MCIp) with follow‐ up up to 48 mo vs. controls and 2) MCI progressors vs. MCI subjects who had not yet progressed to dementia, i.e. MCI non‐progressors (MCInp). Details of the statistical analysis plan for the project are provided in the Biomarkers Consortium ADNI Plasma Proteomics Project Team Data Primer (Included in supplementary materials).

Multivariate analyses were conducted to compare AD versus HC. Within the AD and HC dataset there were 170 subjects who were split into a training (96) and a test (74) set. Assignment to AD and HC classes was based on diagnosis at 12 months . Multivariate analysis approaches utilized included flexible linear discriminant analysis (LDA), logistic regression (LR), partial least squares (PLS), random forests (RF), nearest shrunken centroids (NSC) and support vector machine (SVM) algorithms described in detail in previous reports2. Models were designed, where feasible, to maximize performance on sensitivity. Additionally, two feature selection schemes were used in conjunction with all models except NSC (which includes internal feature selection). First, simple univariate filters were used to reduce the predictor sets prior to modeling (denoted as Filter in the text). To do this, a t‐test was conducted for each predictor to assess whether the average predictor value was different between the classes. Predictors associated with p‐value <0.05 were included in the model. Secondly, recursive feature selection (RFE), or backwards selection, was also used to select an appropriate subset of predictors. Here, each predictor is ranked by an aggregate measure of variable importance. In general, measures of marker importance were biased towards those that used uncertainty (e.g. LR slope tests) versus those that did not (e.g. RF). A model with the full predictor set is used and subsequent models remove less important predictors in batches. Model performance is tracked as the predictor set is reduced to determine an

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optimal number of predictors and the top number of predictors is then included in the final model used for prediction.

Five repeats of ten–fold cross–validation were used to evaluate the performance of the models using the training set. These performance measures were used to finalize values of model tuning parameters as rough estimates of model performance using the training set. The test set was not utilized until a final estimate of model performance was needed. For the feature selection methods, this same procedure was used to incorporate the uncertainty due to feature selection into the performance estimates2. For example, the univariate filters were conducted 50 times (for each of the re‐sampled data sets) to estimate performance, but the final predictor set was determined by applying the univariate filters to the full training set. This suite of modeling methods was applied to three sets of predictors:

 plasma biomarker results plus age, gender and ApoE4 allele (referred to as all)

 age, gender and ApoE4 genetic status alone referred to as baseline model

 plasma Myriad RBM assay results only

Using these models, the predictive ability of the assays were examined beyond demographic and genotype information currently used to assess risk.

1. Storey J. The positive false discovery rate: A bayesian interpretation and the q‐value. Annals of Statistics. 2003;31(6):2013‐35.

2. Saeys Y, Inza I, Larranaga P. A review of feature selection techniques in bioinformatics. Bioinformatics. 2007 Oct 1;23(19):2507‐17.

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