ArticleAnnals of clinical and translational neurology2026
Plasma Proteomic Signatures for Alzheimer's Disease: Comparable Accuracy to ATN Biomarkers and Cross-Platform Validation.
Article in Annals of clinical and translational neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
backgroundThere is growing recognition of the potential of plasma proteomics for Alzheimer's Disease (AD) risk assessment and disease characterization. However, differences between proteomics platforms introduce uncertainties regarding cross-platform applicability.
objectiveWe aimed to identify a detailed plasma biosignature for distinguishing AD from cognitively normal (CN) and another signature for classifying mild cognitive impairment (MCI) decliners and non-decliners. We also explored the cross-platform applicability of these models between two proteomic platforms.
methodsElastic net was performed on 190 plasma analytes measured using the Luminex xMAP platform in 566 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to model MCI stable/decliner and AD/CN classification. MCI decliner was defined as progression to AD during follow-up (mean 4.2 ± 3.2 years). External cross-platform validation was conducted with 1303 participants from the Spanish Ace study, using the SOMAscan 7k platform.
resultsAn 11-analyte signature for distinguishing AD from CN achieved a 93.5% accuracy on ADNI and 95.2% on Ace. The ApoE and BNP proteins were the two most important contributors to the classifier. The MCI classification signature performed less well, with 65.9% accuracy on ADNI and 51.0% accuracy upon validation testing in Ace. DISCUSSION: Compared with prior proteomic-based studies on the same dataset, our findings attained higher specificity and sensitivity for AD classification while utilizing a smaller panel of analytes. We also confirmed the reliability and consistency of this signature within a different population from a different platform. The plasma proteomic platforms explored were, however, not sufficient to determine MCI decliners versus non-decliners.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.