Multiplexed Immunoassay Panel Identifies Novel CSF Biomarkers for Alzheimer's Disease Diagnosis and Prognosis
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{"title"=>"Multiplexed immunoassay panel identifies novel CSF biomarkers for alzheimer's disease diagnosis and prognosis", "type"=>"journal", "authors"=>[{"first_name"=>"Rebecca", "last_name"=>"Craig-Schapiro", "scopus_author_id"=>"12782327800"}, {"first_name"=>"Max", "last_name"=>"Kuhn", "scopus_author_id"=>"36171751800"}, {"first_name"=>"Chengjie", "last_name"=>"Xiong", "scopus_author_id"=>"7102917513"}, {"first_name"=>"Eve H.", "last_name"=>"Pickering", "scopus_author_id"=>"14014649300"}, {"first_name"=>"Jingxia", "last_name"=>"Liu", "scopus_author_id"=>"50361434400"}, {"first_name"=>"Thomas P.", "last_name"=>"Misko", "scopus_author_id"=>"57191471380"}, {"first_name"=>"Richard J.", "last_name"=>"Perrin", "scopus_author_id"=>"23061550800"}, {"first_name"=>"Kelly R.", "last_name"=>"Bales", "scopus_author_id"=>"7005760123"}, {"first_name"=>"Holly", "last_name"=>"Soares", "scopus_author_id"=>"35241097600"}, {"first_name"=>"Anne M.", "last_name"=>"Fagan", "scopus_author_id"=>"7006947447"}, {"first_name"=>"David M.", "last_name"=>"Holtzman", "scopus_author_id"=>"35416882500"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"361671339", "sgr"=>"79955431733", "issn"=>"19326203", "pmid"=>"21526197", "scopus"=>"2-s2.0-79955431733", "doi"=>"10.1371/journal.pone.0018850", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)"}, "id"=>"1a53c782-425e-3516-b1df-9323ed3cb698", "abstract"=>"BACKGROUND: Clinicopathological studies suggest that Alzheimer's disease (AD) pathology begins ∼10-15 years before the resulting cognitive impairment draws medical attention. Biomarkers that can detect AD pathology in its early stages and predict dementia onset would, therefore, be invaluable for patient care and efficient clinical trial design. We utilized a targeted proteomics approach to discover novel cerebrospinal fluid (CSF) biomarkers that can augment the diagnostic and prognostic accuracy of current leading CSF biomarkers (Aβ42, tau, p-tau181).\\n\\nMETHODS AND FINDINGS: Using a multiplexed Luminex platform, 190 analytes were measured in 333 CSF samples from cognitively normal (Clinical Dementia Rating [CDR] 0), very mildly demented (CDR 0.5), and mildly demented (CDR 1) individuals. Mean levels of 37 analytes (12 after Bonferroni correction) were found to differ between CDR 0 and CDR>0 groups. Receiver-operating characteristic curve analyses revealed that small combinations of a subset of these markers (cystatin C, VEGF, TRAIL-R3, PAI-1, PP, NT-proBNP, MMP-10, MIF, GRO-α, fibrinogen, FAS, eotaxin-3) enhanced the ability of the best-performing established CSF biomarker, the tau/Aβ42 ratio, to discriminate CDR>0 from CDR 0 individuals. Multiple machine learning algorithms likewise showed that the novel biomarker panels improved the diagnostic performance of the current leading biomarkers. Importantly, most of the markers that best discriminated CDR 0 from CDR>0 individuals in the more targeted ROC analyses were also identified as top predictors in the machine learning models, reconfirming their potential as biomarkers for early-stage AD. Cox proportional hazards models demonstrated that an optimal panel of markers for predicting risk of developing cognitive impairment (CDR 0 to CDR>0 conversion) consisted of calbindin, Aβ42, and age.\\n\\nCONCLUSIONS/SIGNIFICANCE: Using a targeted proteomic screen, we identified novel candidate biomarkers that complement the best current CSF biomarkers for distinguishing very mildly/mildly demented from cognitively normal individuals. Additionally, we identified a novel biomarker (calbindin) with significant prognostic potential.", "link"=>"http://www.mendeley.com/research/multiplexed-immunoassay-panel-identifies-novel-csf-biomarkers-alzheimers-disease-diagnosis-prognosis-3", "reader_count"=>101, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>6, "Researcher"=>30, "Student > Doctoral Student"=>5, "Student > Ph. D. 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Figshare

  • {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/392111/Table_S1.doc", "https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/392119/Table_S2.doc"], "description"=>"<div><h3>Background</h3><p>Clinicopathological studies suggest that Alzheimer's disease (AD) pathology begins ∼10–15 years before the resulting cognitive impairment draws medical attention. Biomarkers that can detect AD pathology in its early stages and predict dementia onset would, therefore, be invaluable for patient care and efficient clinical trial design. We utilized a targeted proteomics approach to discover novel cerebrospinal fluid (CSF) biomarkers that can augment the diagnostic and prognostic accuracy of current leading CSF biomarkers (Aβ42, tau, p-tau181).</p> <h3>Methods and Findings</h3><p>Using a multiplexed Luminex platform, 190 analytes were measured in 333 CSF samples from cognitively normal (Clinical Dementia Rating [CDR] 0), very mildly demented (CDR 0.5), and mildly demented (CDR 1) individuals. Mean levels of 37 analytes (12 after Bonferroni correction) were found to differ between CDR 0 and CDR>0 groups. Receiver-operating characteristic curve analyses revealed that small combinations of a subset of these markers (cystatin C, VEGF, TRAIL-R3, PAI-1, PP, NT-proBNP, MMP-10, MIF, GRO-α, fibrinogen, FAS, eotaxin-3) enhanced the ability of the best-performing established CSF biomarker, the tau/Aβ42 ratio, to discriminate CDR>0 from CDR 0 individuals. Multiple machine learning algorithms likewise showed that the novel biomarker panels improved the diagnostic performance of the current leading biomarkers. Importantly, most of the markers that best discriminated CDR 0 from CDR>0 individuals in the more targeted ROC analyses were also identified as top predictors in the machine learning models, reconfirming their potential as biomarkers for early-stage AD. Cox proportional hazards models demonstrated that an optimal panel of markers for predicting risk of developing cognitive impairment (CDR 0 to CDR>0 conversion) consisted of calbindin, Aβ42, and age.</p> <h3>Conclusions/Significance</h3><p>Using a targeted proteomic screen, we identified novel candidate biomarkers that complement the best current CSF biomarkers for distinguishing very mildly/mildly demented from cognitively normal individuals. Additionally, we identified a novel biomarker (calbindin) with significant prognostic potential.</p> </div>", "links"=>[], "tags"=>["multiplexed", "immunoassay", "identifies", "csf", "biomarkers", "prognosis"], "article_id"=>137446, "categories"=>["Cell Biology", "Neuroscience", "Biochemistry", "Mental Health", "Mathematics", "Medicine"], "users"=>["Rebecca Craig-Schapiro", "Max Kuhn", "Chengjie Xiong", "Eve H. Pickering", "Jingxia Liu", "Thomas P. Misko", "Richard J. Perrin", "Kelly R. Bales", "Holly Soares", "Anne M. Fagan", "David M. Holtzman"], "doi"=>["http://dx.doi.org/10.1371/journal.pone.0018850"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/Multiplexed_Immunoassay_Panel_Identifies_Novel_CSF_Biomarkers_for_Alzheimer_s_Disease_Diagnosis_and_Prognosis/137446", "title"=>"Multiplexed Immunoassay Panel Identifies Novel CSF Biomarkers for Alzheimer's Disease Diagnosis and Prognosis", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2011-04-19 02:04:06"}
  • {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/783446/Figure_1.tif"], "description"=>"<p>ROC analyses assessed the ability of the traditional biomarkers (blue) and of the 37 RBM analytes with p<0.05 in the univariate analyses (red) to discriminate CDR>0 from CDR 0 individuals. Combining the best-performing of the traditional biomarkers, the tau/Aβ42 ratio, with RBM analytes improved upon the AUC of tau/Aβ42 in many cases (green).</p>", "links"=>[], "tags"=>["graphical"], "article_id"=>453805, "categories"=>["Cell Biology", "Neuroscience", "Biochemistry", "Mental Health", "Mathematics", "Medicine"], "users"=>["Rebecca Craig-Schapiro", "Max Kuhn", "Chengjie Xiong", "Eve H. Pickering", "Jingxia Liu", "Thomas P. Misko", "Richard J. Perrin", "Kelly R. Bales", "Holly Soares", "Anne M. Fagan", "David M. Holtzman"], "doi"=>["http://dx.doi.org/10.1371/journal.pone.0018850.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_ROC_analyses_graphical_representation_/453805", "title"=>"ROC analyses, graphical representation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-04-19 01:03:25"}
  • {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/783537/Figure_2.tif"], "description"=>"<p>For the four models with a built-in importance statistic (i.e., Boosted Tree, Nearest Shrunken Centroids, Random Forests, and Partial Least Squares), there is considerable overlap in the top 15 predictors for each model. Additionally, nearly all of the markers found to best discriminate CDR 0 from CDR>0 participants in the more targeted ROC analyses (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0018850#pone-0018850-t005\" target=\"_blank\">Table 5</a>), as shown here (‘Targeted’), were also identified as the top predictors in the machine learning models.</p>", "links"=>[], "tags"=>["diagram", "15", "predictors", "algorithms", "built-in"], "article_id"=>453894, "categories"=>["Cell Biology", "Neuroscience", "Biochemistry", "Mental Health", "Mathematics", "Medicine"], "users"=>["Rebecca Craig-Schapiro", "Max Kuhn", "Chengjie Xiong", "Eve H. Pickering", "Jingxia Liu", "Thomas P. Misko", "Richard J. Perrin", "Kelly R. Bales", "Holly Soares", "Anne M. Fagan", "David M. Holtzman"], "doi"=>["http://dx.doi.org/10.1371/journal.pone.0018850.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Venn_diagram_of_the_top_15_predictors_for_machine_learning_algorithms_with_a_built_in_importance_measure_/453894", "title"=>"Venn diagram of the top 15 predictors for machine learning algorithms with a built-in importance measure.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-04-19 01:04:54"}
  • {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/783651/Table_6.xls"], "description"=>"<p>Ten statistical machine learning algorithms were used to determine groups of markers capable of distinguishing very mildly/mildly demented (CDR 0.5 and 1 combined) from cognitively normal participants (CDR 0). Models were fit with two sets of predictors: 1) traditional biomarkers, or 2) traditional biomarkers plus RBM analytes; additionally, age, gender, and ApoE4 allele status were included in all models. Model performance measures shown are based on cross-validation, in which the test set results were averaged from 200 splits of the data between training (80%) and test (20%).</p>", "links"=>[], "tags"=>["measures", "algorithms", "discriminating", "cognitively", "demented"], "article_id"=>454010, "categories"=>["Cell Biology", "Neuroscience", "Biochemistry", "Mental Health", "Mathematics", "Medicine"], "users"=>["Rebecca Craig-Schapiro", "Max Kuhn", "Chengjie Xiong", "Eve H. Pickering", "Jingxia Liu", "Thomas P. Misko", "Richard J. Perrin", "Kelly R. Bales", "Holly Soares", "Anne M. Fagan", "David M. Holtzman"], "doi"=>["http://dx.doi.org/10.1371/journal.pone.0018850.t006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Performance_measures_of_machine_learning_algorithms_in_discriminating_cognitively_normal_CDR_0_from_very_mildly_mildly_demented_CDR_0_5_and_1_participants_/454010", "title"=>"Performance measures of machine learning algorithms in discriminating cognitively normal (CDR 0) from very mildly/mildly demented (CDR 0.5 and 1) participants.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-04-19 01:06:50"}
  • {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/783705/Table_7.xls"], "description"=>"<p>Ranking of the top 15 predictors for the four models with a built-in importance statistic demonstrates considerable overlap in the top predictors for each model. Furthermore, nearly all of the markers found to best discriminate CDR 0 from CDR>0 participants in the more targeted ROC analyses (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0018850#pone-0018850-t005\" target=\"_blank\">Table 5</a>) were also identified as the top predictors in the machine learning models, reconfirming their biomarker potential.</p>", "links"=>[], "tags"=>["15", "predictors", "algorithms", "built-in"], "article_id"=>454061, "categories"=>["Cell Biology", "Neuroscience", "Biochemistry", "Mental Health", "Mathematics", "Medicine"], "users"=>["Rebecca Craig-Schapiro", "Max Kuhn", "Chengjie Xiong", "Eve H. Pickering", "Jingxia Liu", "Thomas P. Misko", "Richard J. Perrin", "Kelly R. Bales", "Holly Soares", "Anne M. Fagan", "David M. Holtzman"], "doi"=>["http://dx.doi.org/10.1371/journal.pone.0018850.t007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Top_15_predictors_for_machine_learning_algorithms_with_a_built_in_importance_measure_/454061", "title"=>"Top 15 predictors for machine learning algorithms with a built-in importance measure.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-04-19 01:07:41"}
  • {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/783752/Table_4.xls"], "description"=>"<p>To assess the ability of the markers to distinguish CDR>0 from CDR 0, ROC analyses were performed for each of the traditional biomarkers (Aβ42, tau, p-tau181 and the ratios tau/Aβ42 and p-tau181/Aβ42) and for the 37 RBM analytes with p<0.05 in the univariate analyses. Each traditional biomarker was then combined with each RBM analyte to identify ‘2-marker panels’ with improved AUCs. Among the traditional biomarkers, the ratios tau/Aβ42 and p-tau181/Aβ42 demonstrated the highest AUCs; additionally, combining these ratios with the RBM analytes consistently yielded 2-marker panels with AUCs higher than combinations of the individual traditional biomarkers (Aβ42, tau, p-tau181) with the RBM analytes. Thus, only the most promising 2-marker panels (those with tau/Aβ42 and p-tau181/Aβ42) are shown here. To determine whether combinations of three markers could yield a small panel with improved diagnostic accuracy, the four 2-marker panels with the highest AUCs were combined with the 10 RBM analytes with the highest individual AUCs (indicated by §, results in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0018850#pone-0018850-t005\" target=\"_blank\">Table 5</a>).</p>", "links"=>[], "tags"=>["Mental health", "pathology", "neurological disorders", "Biochemistry", "mathematics", "Non-clinical medicine"], "article_id"=>454107, "categories"=>["Cell Biology", "Neuroscience", "Biochemistry", "Mental Health", "Mathematics", "Medicine"], "users"=>["Rebecca Craig-Schapiro", "Max Kuhn", "Chengjie Xiong", "Eve H. Pickering", "Jingxia Liu", "Thomas P. Misko", "Richard J. Perrin", "Kelly R. Bales", "Holly Soares", "Anne M. Fagan", "David M. Holtzman"], "doi"=>["http://dx.doi.org/10.1371/journal.pone.0018850.t004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_ROC_analyses_/454107", "title"=>"ROC analyses.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-04-19 01:08:27"}
  • {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/783804/Table_5.xls"], "description"=>"<p>AUC =  area under the curve; Stdev =  standard deviation; CI =  confidence interval.</p><p>Receiver operating characteristic (ROC) analyses assessed the ability of three marker panels to discriminate CDR 0 from CDR>0 participants. Averages of performance measures were taken over 100 iterations of the bootstrap. “p-value” assesses the difference between the three marker panel and the corresponding two marker panel (e.g. log tau/Aβ42 + log Cystatin C + TRAIL-R3 vs. log tau/Aβ42 + log Cystatin C).</p>", "links"=>[], "tags"=>["analyses", "3-marker"], "article_id"=>454164, "categories"=>["Cell Biology", "Neuroscience", "Biochemistry", "Mental Health", "Mathematics", "Medicine"], "users"=>["Rebecca Craig-Schapiro", "Max Kuhn", "Chengjie Xiong", "Eve H. Pickering", "Jingxia Liu", "Thomas P. Misko", "Richard J. Perrin", "Kelly R. Bales", "Holly Soares", "Anne M. Fagan", "David M. Holtzman"], "doi"=>["http://dx.doi.org/10.1371/journal.pone.0018850.t005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_ROC_analyses_of_3_marker_panels_/454164", "title"=>"ROC analyses of 3-marker panels.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-04-19 01:09:24"}
  • {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/783845/Table_2.xls"], "description"=>"<p>Analysis of covariance (ANCOVA) using the General Linear Model (GLM) procedure in SAS was used to determine analytes that differed in concentration (p<0.05) between CDR 0 and CDR>0 groups while adjusting for the effects of age and gender (\"adjusted means\").</p><p>*indicates analytes that were significant after Bonferroni correction based on the number of markers analyzed (128 markers, cutoff of 0.0004 for familywise p<0.05). For markers that were log transformed to approximate a normal distribution, the resulting Least Squares mean (or estimated marginal mean) was back-transformed to obtain the adjusted mean shown. Also provided are the raw mean concentrations for the CDR 0 and CDR>0 groups.</p>", "links"=>[], "tags"=>["levels", "cognitively", "demented"], "article_id"=>454207, "categories"=>["Cell Biology", "Neuroscience", "Biochemistry", "Mental Health", "Mathematics", "Medicine"], "users"=>["Rebecca Craig-Schapiro", "Max Kuhn", "Chengjie Xiong", "Eve H. Pickering", "Jingxia Liu", "Thomas P. Misko", "Richard J. Perrin", "Kelly R. Bales", "Holly Soares", "Anne M. Fagan", "David M. Holtzman"], "doi"=>["http://dx.doi.org/10.1371/journal.pone.0018850.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Analytes_that_differ_in_levels_between_cognitively_normal_CDR_0_and_very_mildly_mildly_demented_CDR_0_5_and_1_participants_/454207", "title"=>"Analytes that differ in levels between cognitively normal (CDR 0) and very mildly/mildly demented (CDR 0.5 and 1) participants.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-04-19 01:10:07"}
  • {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/783894/Table_3.xls"], "description"=>"<p>Correlations were evaluated using the Spearman rho correlation coefficient (α = 0.05); shown are the <i>r</i> and (p value). Gender differences were evaluated by Mann-Whitney test.</p>", "links"=>[], "tags"=>["rbm", "analytes", "biomarker"], "article_id"=>454254, "categories"=>["Cell Biology", "Neuroscience", "Biochemistry", "Mental Health", "Mathematics", "Medicine"], "users"=>["Rebecca Craig-Schapiro", "Max Kuhn", "Chengjie Xiong", "Eve H. Pickering", "Jingxia Liu", "Thomas P. Misko", "Richard J. Perrin", "Kelly R. Bales", "Holly Soares", "Anne M. Fagan", "David M. Holtzman"], "doi"=>["http://dx.doi.org/10.1371/journal.pone.0018850.t003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Correlations_of_RBM_analytes_with_age_gender_and_other_biomarker_values_/454254", "title"=>"Correlations of RBM analytes with age, gender, and other biomarker values.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-04-19 01:10:54"}
  • {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/783938/Table_1.xls"], "description"=>"<p>Abbreviations: CDR, Clinical Dementia Rating; <i>APOE</i>, apolipoprotein E; MMSE, Mini-Mental State Examination; LP, lumbar puncture; SD, standard deviation; CSF, cerebrospinal fluid; Aβ-42, amyloid-beta peptide 1-42; p-tau181, tau phosphorylated at threonine 181; PIB MCBP, Pittsburgh Compound B mean cortical PIB binding potential. MCBP data available for 179 study participants.</p>", "links"=>[], "tags"=>["genotypic", "characteristics", "333"], "article_id"=>454296, "categories"=>["Cell Biology", "Neuroscience", "Biochemistry", "Mental Health", "Mathematics", "Medicine"], "users"=>["Rebecca Craig-Schapiro", "Max Kuhn", "Chengjie Xiong", "Eve H. Pickering", "Jingxia Liu", "Thomas P. Misko", "Richard J. Perrin", "Kelly R. Bales", "Holly Soares", "Anne M. Fagan", "David M. Holtzman"], "doi"=>["http://dx.doi.org/10.1371/journal.pone.0018850.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Demographic_clinical_and_genotypic_characteristics_of_the_333_study_participants_/454296", "title"=>"Demographic, clinical, and genotypic characteristics of the 333 study participants.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-04-19 01:11:36"}
  • {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/784000/Table_8.xls"], "description"=>"<p>Cox proportional hazards models were used to identify panels of biomarkers predictive of the risk of developing cognitive impairment (conversion from CDR 0 to CDR>0). Analyte measurements were converted to standard Z-scores to allow for comparison of hazard ratios between the different analytes. Variables with p<0.15 in the univariate Cox analyses were considered for inclusion in multivariate models; variables were retained in the final model if p<0.05. Because many of the analytes, including calbindin, demonstrated age and gender affects, both variables were entered into the multivariate models. However, as gender did not appear to contribute to the models (A, D), it was not included in the final panels (C, E). Similarly, apoE allelic status (E4+ vs. E4−) did not contribute to the models (B), and was not included in the final model (C). Although calbindin and tau both demonstrated p<0.05 in the univariate analyses, the significant correlation between the two (<i>r</i> = 0.476, p<0.0001) prohibited inclusion of both variables in the multivariate model. Therefore, a separate multivariate model that included tau was evaluated (D, E). The higher HR of calbindin than of tau, and the higher overall HR (4.704>3.610) and lower AIC (227.6<230.8) of the first model support it as the better model.</p>", "links"=>[], "tags"=>["proportional", "hazards", "models", "predicting", "impairment", "cdr"], "article_id"=>454350, "categories"=>["Cell Biology", "Neuroscience", "Biochemistry", "Mental Health", "Mathematics", "Medicine"], "users"=>["Rebecca Craig-Schapiro", "Max Kuhn", "Chengjie Xiong", "Eve H. Pickering", "Jingxia Liu", "Thomas P. Misko", "Richard J. Perrin", "Kelly R. Bales", "Holly Soares", "Anne M. Fagan", "David M. Holtzman"], "doi"=>["http://dx.doi.org/10.1371/journal.pone.0018850.t008"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Cox_proportional_hazards_models_for_predicting_risk_of_developing_cognitive_impairment_conversion_from_CDR_0_to_CDR_gt_0_/454350", "title"=>"Cox proportional hazards models for predicting risk of developing cognitive impairment (conversion from CDR 0 to CDR>0).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-04-19 01:12:30"}

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  • {"unique-ip"=>"12", "full-text"=>"10", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"4", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
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  • {"unique-ip"=>"18", "full-text"=>"18", "pdf"=>"7", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
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Relative Metric

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