Evidence map›Paper›PMID 36847005›Full record

ArticleJournal of Alzheimer's disease : JAD2023

Early Detection of Alzheimer's Disease-Related Pathology Using a Multi-Disease Diagnostic Platform Employing Autoantibodies as Blood-Based Biomarkers.

Cassandra A DeMarshall, Jeffrey Viviano, Sheina Emrani, Umashanger Thayasivam, George A Godsey, Abhirup Sarkar, Benjamin Belinka, David J Libon, Robert G Nagele, Parkinson’s Study Group and the Alzheimer’s Disease Neuroimaging Initiative

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of Alzheimer's disease : JAD, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.5field-weighted citation impact, top 11% 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

7 citing papers in PubMed, 14 citations in OpenAlex.

  1. Machine learning to detect Alzheimer's disease with data on drugs and diagnoses.The journal of prevention of Alzheimer's disease · 2025
    Article
  2. Article
  3. Review
  4. Fluid biomarkers in cerebral amyloid angiopathy.Frontiers in neuroscience · 2024
    Review
  5. Article
  6. Review
  7. Article
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

10 authors at 2 institutions in 1 country.

Cassandra A DeMarshallDurin Technologies, Inc., Mullica Hill, NJ, USA.
Jeffrey VivianoDurin Technologies, Inc., Mullica Hill, NJ, USA.
Sheina EmraniNew Jersey Institute for Successful Aging, Rowan University, Stratford, NJ, Department of Psychology, Rowan University, Glassboro, NJ, USA.
Umashanger ThayasivamDurin Technologies, Inc., Mullica Hill, NJ, USA.
George A GodseyDurin Technologies, Inc., Mullica Hill, NJ, USA.
Abhirup SarkarDurin Technologies, Inc., Mullica Hill, NJ, USA.
Benjamin BelinkaDurin Technologies, Inc., Mullica Hill, NJ, USA.
David J LibonNew Jersey Institute for Successful Aging, Rowan University, Stratford, NJ, Department of Psychology, Rowan University, Glassboro, NJ, USA.
Robert G NageleDurin Technologies, Inc., Mullica Hill, NJ, USA.
Parkinson’s Study Group and the Alzheimer’s Disease Neuroimaging Initiative
Rowan University · USBrown University · US

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
NIA NIH HHS U01 AG024904
6 · The paper itself

Abstract

backgroundEvidence for the universal presence of IgG autoantibodies in blood and their potential utility for the diagnosis of Alzheimer's disease (AD) and other neurodegenerative diseases has been extensively demonstrated by our laboratory. The fact that AD-related neuropathological changes in the brain can begin more than a decade before tell-tale symptoms emerge has made it difficult to develop diagnostic tests useful for detecting the earliest stages of AD pathogenesis.

objectiveTo determine the utility of a panel of autoantibodies for detecting the presence of AD-related pathology along the early AD continuum, including at pre-symptomatic [an average of 4 years before the transition to mild cognitive impairment (MCI)/AD)], prodromal AD (MCI), and mild-moderate AD stages.

methodsA total of 328 serum samples from multiple cohorts, including ADNI subjects with confirmed pre-symptomatic, prodromal, and mild-moderate AD, were screened using Luminex xMAP® technology to predict the probability of the presence of AD-related pathology. A panel of eight autoantibodies with age as a covariate was evaluated using randomForest and receiver operating characteristic (ROC) curves.

resultsAutoantibody biomarkers alone predicted the probability of the presence of AD-related pathology with 81.0% accuracy and an area under the curve (AUC) of 0.84 (95% CI = 0.78-0.91). Inclusion of age as a parameter to the model improved the AUC (0.96; 95% CI = 0.93-0.99) and overall accuracy (93.0%).

conclusionBlood-based autoantibodies can be used as an accurate, non-invasive, inexpensive, and widely accessible diagnostic screener for detecting AD-related pathology at pre-symptomatic and prodromal AD stages that could aid clinicians in diagnosing AD.

Indexed as

Alzheimer DiseaseCognitive DysfunctionAutoantibodiesBiomarkersHumansROC CurveAutoantibodiesBiomarkersAlzheimer’s diseaseantibodyautoantibodiesbiomarkersblood-based biomarkersdiagnosticsearly diagnosismild cognitive impairment

Identifiers

PMID36847005
PMCPMC10116135
OpenAlexW4321445608

What OpenQuestion holds

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LicenceCC BY
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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.