ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2025
Longitudinal plasma proteomics: relation to incident Alzheimer's disease dementia and biomarkers.
Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Deep Plasma Proteomics-Based Diagnostic Panel for Early Detection of Amnestic Mild Cognitive Impairment.Journal of proteome research · 2026Article
- Longitudinal plasma proteomics separates diagnostic differences from progression-linked changes in Alzheimer's disease.medRxiv : the preprint server for health sciences · 2026Article
- Longitudinal plasma proteomics: relation to incident Alzheimer's disease dementia and biomarkers.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
introductionWe investigated whether longitudinal changes in plasma proteins were associated with baseline cognitive stages related to Alzheimer's disease (AD), their progression, and AD biomarkers.
methodsWe analyzed longitudinal proteomics (SomaScan 7K) data (N = 347) from the Indiana AD Research Center using linear mixed-effects models for associations with baseline cognitive stages, AD dementia (ADD) conversion, and AD imaging/plasma biomarkers, followed by machine learning analysis to evaluate predictive performance for incident ADD.
resultsOur analysis identified two proteins (ACES and IGFALS) associated with baseline diagnosis stages and six proteins (ACES, C7, ZCD1, IL-17C, CC055, and SO5A1) associated with incident ADD. Longitudinal changes of the identified proteins were also associated with AD imaging/plasma biomarkers. The inclusion of longitudinal protein changes yielded an AUC of 84.8% for predicting incident ADD.
conclusionOur findings showed molecular signatures for AD progression and the potential of dynamic changes in plasma proteins as biomarkers for predicting incident ADD. HIGHLIGHTS: Changes in plasma ACES and IGFALS linked to baseline AD cognitive stages Changes in ACES, C7, ZCD1, IL-17C, CC055, and SO5A1 associated with incident ADD Changes in those proteins correlated with baseline AD imaging and plasma biomarkers Proteomics model achieved 84.8% AUC-ROC in predicting incident ADD.
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.