ArticlebioRxiv : the preprint server for biology2026
Single-subject Proteomic Signatures in Alzheimer's Disease Reflect Clinical Phenotypes and Distinguish Asymptomatic from Symptomatic Cases.
Article in bioRxiv : the preprint server for biology, 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
- Updated by
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAlzheimer's Disease (AD) exhibits considerable inter-individual variability in clinical presentation, neuropathological burden, and underlying molecular processes. Conventional cohort-based analyses of omics molecular data often mask individual-level heterogeneity, limiting insights into the precision therapeutic strategies. To address this challenge, we developed INDIGO (INdividual-level DIfferential GenOmics), a computational framework that quantifies molecular deviations for each individual relative to healthy controls, enabling subject-specific profiling of diseaseassociated alterations in proteomic data, with a framework that is readily applicable to other omics modalities.
methodsWe applied INDIGO to dorsolateral prefrontal cortex (DLPFC) proteomic data from ROSMAP cohort (N = 610). Protein level deviations were aggregated into gene set activity scores for KEGG pathways and curated AD Biodomain annotations. Functional alterations across AD and Asymptomatic AD (AsymAD) individuals were evaluated and correlated with clinical metrics including APOE4 genotype, Braak stage, CERAD and MMSE scores. Graph-based clustering was used to identify molecularly distinct subgroups based on shared patterns of functional dysregulation.
resultsLimited overlap was observed between cohort-level differential expression analysis and INDIGO single-subject analyses. Individual deviations in various processes, including metabolic, immune and epigenetic pathways, exhibited sex- and disease stage-specific patterns. Amyloid clearance and immune activation were strongly associated with APOE4 dosage, higher amyloid and tau burden and cognitive decline, whereas upregulation of mitochondrial and synaptic modules correlated positively with preserved cognitive function. By linking individuals through concordant directional proteomic changes, we identified molecularly coherent subgroups that transcend conventional diagnostic boundaries and include both AD and AsymAD subjects. Each subgroup displayed distinct functional signatures and defined by a unique set of key regulatory proteins. DISCUSSION: These results demonstrate that single-subject omics profiling can resolve individual molecular signatures aligned with clinical and neuropathological variation in AD. By linking molecular heterogeneity with disease phenotypes, INDIGO provides a scalable framework for precision modeling and novel therapeutic target discovery.
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.