ArticleNeuroImage2014
Imaging proteomics for diagnosis, monitoring and prediction of Alzheimer's disease.
Article in NeuroImage, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- A Systematic Review of Longitudinal Studies Which Measure Alzheimer's Disease Biomarkers.Journal of Alzheimer's disease : JAD · 2017Pooled it
- Identifying proteomic prognostic markers for Alzheimer's disease with survival machine learning: The Framingham Heart Study.The journal of prevention of Alzheimer's disease · 2025Article
- Association of plasma BMP6 levels with the rates of brain atrophy in older people without dementia.Frontiers in neurology · 2025Article
- Identifying Proteomic Prognostic Markers for Alzheimer's Disease with Survival Machine Learning: the Framingham Heart Study.medRxiv : the preprint server for health sciences · 2024Article
- Which neuroimaging and fluid biomarkers method is better in theranostic of Alzheimer's disease? An umbrella review.IBRO neuroscience reports · 2024Review
- Is an MRI-derived anatomical measure of dementia risk also a measure of brain aging?GeroScience · 2023Article
- Brain transcriptomic profiling reveals common alterations across neurodegenerative and psychiatric disorders.Computational and structural biotechnology journal · 2022Article
- Multivariate Analysis and Modelling of multiple Brain endOphenotypes: Let's MAMBO!Computational and structural biotechnology journal · 2021Review
- Recent Evidence in Epigenomics and Proteomics Biomarkers for Early and Minimally Invasive Diagnosis of Alzheimer's and Parkinson's Diseases.Current neuropharmacology · 2021Review
- Why Inclusion Matters for Alzheimer's Disease Biomarker Discovery in Plasma.Journal of Alzheimer's disease : JAD · 2021Article
- Omics-based Biomarkers for the Early Alzheimer Disease Diagnosis and Reliable Therapeutic Targets Development.Current neuropharmacology · 2019Article
- Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2017Review
- Changes in the plasma proteome at asymptomatic and symptomatic stages of autosomal dominant Alzheimer's disease.Scientific reports · 2016Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Proteomic and imaging markers have been widely studied as potential biomarkers for diagnosis, monitoring and prognosis of Alzheimer's disease. In this study, we used Alzheimer Disease Neuroimaging Initiative dataset and performed parallel independent component analysis on cross sectional and longitudinal proteomic and imaging data in order to identify the best proteomic model for diagnosis, monitoring and prediction of Alzheimer disease (AD). We used plasma proteins measurement and imaging data from AD and healthy controls (HC) at the baseline and 1 year follow-up. Group comparisons at baseline and changes over 1 year were calculated for proteomic and imaging data. The results were fed into parallel independent component analysis in order to identify proteins that were associated with structural brain changes cross sectionally and longitudinally. Regression model was used to find the best model that can discriminate AD from HC, monitor AD and to predict MCI converters from non-converters. We showed that five proteins are associated with structural brain changes in the brain. These proteins could discriminate AD from HC with 57% specificity and 89% sensitivity. Four proteins whose change over 1 year were associated with brain structural changes could discriminate AD from HC with sensitivity of 93%, and specificity of 92%. This model predicted MCI conversion to AD in 2 years with 94% accuracy. This model has the highest accuracy in prediction of MCI conversion to AD within the ADNI-1 dataset. This study shows that combination of selected plasma protein levels and MR imaging is a useful method in identifying potential biomarker.
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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.