Evidence map›Paper›PMID 38047367›Full record

ArticleCurrent Alzheimer research2023

Blood Biomarkers Discriminate Cerebral Amyloid Status and Cognitive Diagnosis when Collected with ACD-A Anticoagulant.

Zachary D Green, Paul J Kueck, Casey S John, Jeffrey M Burns, Jill K Morris

Open access · hybridAbstract read
In one paragraph

Article in Current Alzheimer research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.2field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Machine learning based on event-related oscillations of working memory differentiates between preclinical Alzheimer's disease and normal aging.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Zachary D GreenAlzheimer's Disease Research Center, University of Kansas, Kansas City, KS, 66160, United States.ORCID 0000-0003-2141-4109
Paul J KueckAlzheimer's Disease Research Center, University of Kansas, Kansas City, KS, 66160, United States.
Casey S JohnAlzheimer's Disease Research Center, University of Kansas, Kansas City, KS, 66160, United States.
Jeffrey M BurnsAlzheimer's Disease Research Center, University of Kansas, Kansas City, KS, 66160, United States.
Jill K MorrisAlzheimer's Disease Research Center, University of Kansas, Kansas City, KS, 66160, United States.ORCID 0000-0001-5911-4190
University of Kansas · US

Funding

University of Kansas Alzheimer's Disease Research Center (KU ADRC)P30AG072973 · NIA · UNIVERSITY OF KANSAS MEDICAL CENTER · PI Jaime Perales Puchalt · 2021 to 2026
$25.3M
Using Integrated Omics to Identify Dysfunctional Genetic Mechanisms Influencing Schizophrenia and Sleep DisturbancesP20GM130423 · NIGMS · UNIVERSITY OF KANSAS MEDICAL CENTER · PI Devin Charles Koestler · 2019 to 2026
$21.5M
University of Kansas Alzheimer's Disease Core CentersP30AG035982 · NIA · UNIVERSITY OF KANSAS MEDICAL CENTER · PI BROOKS, WILLIAM M. · 2011 to 2020
$16.6M
Prescribing Smart Aging: Integrating Health Systems with Community-Based Lifestyle InterventionsR01AG052954 · NIA · UNIVERSITY OF KANSAS MEDICAL CENTER · PI BURNS, JEFFREY MURRAY · 2017 to 2021
$2.9M
Acute exercise and the cerebral metabolic response in aging and Alzheimer's DiseaseR01AG062548 · NIA · UNIVERSITY OF KANSAS MEDICAL CENTER · PI MORRIS, JILL KATHLEEN · 2020 to 2024
$2.4M
Feasibility of Improving Glycemia to Prevent Alzheimer's DiseaseR01AG081304 · NIA · UNIVERSITY OF KANSAS MEDICAL CENTER · PI Paige C Geiger, Jill Kathleen Morris · 2023 to 2026
$1.9M
Effects of metabolic phenotype on functional connectivity in aging and Alzheimer’s DiseaseF30AG076278 · NIA · UNIVERSITY OF KANSAS MEDICAL CENTER · PI GREEN, ZACHARY DOUGLASS · 2022 to 2025
$180k
NIA NIH HHS F30 AG076278NIA NIH HHS P30 AG035982NIA NIH HHS P30 AG072973NIA NIH HHS R01 AG052954NIA NIH HHS R01 AG062548NIA NIH HHS R01 AG081304NIGMS NIH HHS P20 GM130423
6 · The paper itself

Abstract

backgroundThe development of biomarkers that are easy to collect, process, and store is a major goal of research on current Alzheimer's Disease (AD) and underlies the growing interest in plasma biomarkers. Biomarkers with these qualities will improve diagnosis and allow for better monitoring of therapeutic interventions. However, blood collection strategies have historically differed between studies. We examined the ability of various ultrasensitive plasma biomarkers to predict cerebral amyloid status in cognitively unimpaired individuals when collected using acid citrate dextrose (ACD). We then examined the ability of these biomarkers to predict cognitive impairment independent of amyloid status.

methodsUsing a cross-sectional study design, we measured amyloid beta 42/40 ratio, pTau-181, neurofilament-light, and glial fibrillary acidic protein using the Quanterix Simoa® HD-X platform. To evaluate the discriminative accuracy of these biomarkers in determining cerebral amyloid status, we used both banked plasma and 18F-AV45 PET cerebral amyloid neuroimaging data from 140 cognitively unimpaired participants. We further examined their ability to discriminate cognitive status by leveraging data from 42 cognitively impaired older adults. This study is the first, as per our knowledge, to examine these specific tests using plasma collected using acid citrate dextrose (ACD), as well as the relationship with amyloid PET status.

resultsPlasma AB42/40 had the highest AUC (0.833, 95% C.I. 0.767-0.899) at a cut-point of 0.0706 for discriminating between the two cerebral amyloid groups (sensitivity 76%, specificity 78.5%). Plasma NFL at a cut-point of 20.58pg/mL had the highest AUC (0.908, 95% CI 0.851- 0.966) for discriminating cognitive impairment (sensitivity 84.8%, specificity 89.9%). The addition of age and apolipoprotein e4 status did not improve the discriminative accuracy of these biomarkers.

conclusionOur results suggest that the Aβ42/40 ratio is useful in discriminating clinician-rated elevated cerebral amyloid status and that NFL is useful for discriminating cognitive impairment status. These findings reinforce the growing body of evidence regarding the general utility of these biomarkers and extend their utility to plasma collected in a non-traditional anticoagulant.

Indexed as

Alzheimer DiseaseCognitive DysfunctionAgedAmyloidAmyloid beta-PeptidesAnticoagulantsBiomarkersCitric AcidCognitionCross-Sectional StudiesGlucoseHumanstau Proteinsacid citrate dextroseAmyloidAmyloid beta-PeptidesAnticoagulantsBiomarkersCitric AcidGlucosetau ProteinsACDAlzheimer’s diseaseamyloidanticoagulant.biomarkersplasmaprocessing

Identifiers

PMID38047367
PMCPMC10792989
OpenAlexW4389300301

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.