Literature DB >> 22963265

Biomarker positive and negative subjects in the ADNI cohort: clinical characterization.

Richard E Kennedy1, Lon S Schneider, Gary R Cutter.   

Abstract

BACKGROUND: The Alzheimer's Disease Neuroimaging Initiative (ADNI) was created to develop standards for brain imaging and biomarkers for diagnosis and treatment trials. Using the ADNI dataset, experts have found that low cerebrospinal fluid amyloid-β1-42 (CSF Aβ1-42) concentration and high total-tau/Aβ1-42 ratio are highly predictive of progression in amnestic mild cognitive impairment (aMCI), and recommended these biomarkers to support the diagnosis of prodromal Alzheimer's disease and select patients for clinical trials. However, biomarker selection criteria may introduce systematic bias that undermines their utility.
METHODS: We tested for systematic biases among individuals undergoing lumbar puncture in the ADNI dataset who fulfilled the following entry criteria: (1) aMCI with CSF Aβ1-42 ≤ 192 pG/mL, compared to aMCI with Aβ1-42 > 192 pG/mL, and (2) aMCI with total-tau/Aβ1-42 > 0.39, compared to aMCI with total-tau/Aβ1-42 ≤ 0.39, as well as comparisons between participants with aMCI with and without lumbar puncture.
FINDINGS: Individuals with low CSF Aβ1-42 scored significantly poorer than individuals with high Aβ1-42 on several baseline measures of disease severity, including Logical Memory II (3.24 vs 4.73; p < 0.001), Functional Activities Questionnaire (4.30 vs 2.37; p < 0.001), and Alzheimer's Disease Assessment Scale-cognitive (12.23 vs 10.09; p=0.002). Similar results were found using high total-tau/Aβ1-42. No differences were found for individuals with and without lumbar puncture except for marital status. INTERPRETATIONS: Individuals with aMCI with low Aβ1-42 in the ADNI dataset appear to have more advanced disease than those with high Aβ1-42. Selection criteria based on ADNI, as well as design of future studies, must account for potential confounds between biomarker status and disease severity to ensure that the former, and not the latter, is the true determinant of predictive accuracy.

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Year:  2012        PMID: 22963265     DOI: 10.2174/156720512804142976

Source DB:  PubMed          Journal:  Curr Alzheimer Res        ISSN: 1567-2050            Impact factor:   3.498


  7 in total

1.  Simulating effects of biomarker enrichment on Alzheimer's disease prevention trials: conceptual framework and example.

Authors:  Jeannie-Marie S Leoutsakos; Alexandra L Bartlett; Sarah N Forrester; Constantine G Lyketsos
Journal:  Alzheimers Dement       Date:  2013-08-15       Impact factor: 21.566

2.  Progression of Alzheimer's Disease by Self-Reported Cancer History in the Alzheimer's Disease Neuroimaging Initiative.

Authors:  Mackenzie E Fowler; Kristen L Triebel; Gary R Cutter; Lon S Schneider; Richard E Kennedy
Journal:  J Alzheimers Dis       Date:  2020       Impact factor: 4.472

3.  Cerebrospinal Fluid proNGF: A Putative Biomarker for Early Alzheimer's Disease.

Authors:  Scott E Counts; Bin He; John G Prout; Bernadeta Michalski; Lucia Farotti; Margaret Fahnestock; Elliott J Mufson
Journal:  Curr Alzheimer Res       Date:  2016       Impact factor: 3.498

4.  Amyloid-β Peptides and Tau Protein as Biomarkers in Cerebrospinal and Interstitial Fluid Following Traumatic Brain Injury: A Review of Experimental and Clinical Studies.

Authors:  Parmenion P Tsitsopoulos; Niklas Marklund
Journal:  Front Neurol       Date:  2013-06-26       Impact factor: 4.003

5.  Serial position effects in the Logical Memory Test: Loss of primacy predicts amyloid positivity.

Authors:  Davide Bruno; Kimberly D Mueller; Tobey Betthauser; Nathaniel Chin; Corinne D Engelman; Bradley Christian; Rebecca L Koscik; Sterling C Johnson
Journal:  J Neuropsychol       Date:  2020-12-04       Impact factor: 2.276

Review 6.  The Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog): Modifications and Responsiveness in Pre-Dementia Populations. A Narrative Review.

Authors:  Jacqueline K Kueper; Mark Speechley; Manuel Montero-Odasso
Journal:  J Alzheimers Dis       Date:  2018       Impact factor: 4.472

7.  A recommended "minimum data set" framework for SD-OCT retinal image acquisition and analysis from the Atlas of Retinal Imaging in Alzheimer's Study (ARIAS).

Authors:  Jessica Alber; Edmund Arthur; Stuart Sinoff; Delia Cabrera DeBuc; Emily Y Chew; Lori Douquette; Wendy V Hatch; Chris Hudson; Amir Kashani; Cecelia S Lee; Stephen Montaquila; Sima Mozdbar; Leonardo Provetti Cunha; Faryan Tayyari; Gregory Van Stavern; Peter J Snyder
Journal:  Alzheimers Dement (Amst)       Date:  2020-11-01
  7 in total

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