Integrating Divergence-Based Proteomic Analysis and Directed Network Diffusion to Characterize Diagnosis-Anchored Molecular Variability at the Metabolic Syndrome-Migraine Interface.

Overview

Authors: Bei Wang¹, Yulin Li¹, Yixing Liu², Dongran Han^1,2

ORCID iDs: Bei Wang, Yulin Li, Yixing Liu, Dongran Han

School of Life Sciences, Beijing University of Chinese Medicine, Beijing 102488, China; (B.W.); (Y.L.)
School of Management, Beijing University of Chinese Medicine, Beijing 102488, China

Institutions: Beijing University of Chinese Medicine (China)

Journal: International journal of molecular sciences, volume 27, issue 11, article 4820

Dates: received 14 April 2026; accepted 22 May 2026; published online 27 May 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.3390/ijms27114820 · PMID 42278351 · PMCID PMC13257391 · OpenAlex W7162501699

Open access: gold, a free copy (OpenAlex)

Status: data only

Categories: genetics / omics (modality), human (organism), pain (population)

Methods: Connectivity, Statistics, Machine learning, Spectral & time-frequency, Graphs

Keywords: migraine, metabolic syndrome, plasma proteomics, proteomic variability, divergence-based proteomic analysis, directed network diffusion, network medicine

MeSH: Metabolic Syndrome*, Migraine Disorders*, Proteome*, Proteomics*, Case-Control Studies, Female, Humans, Male, Middle Aged (* major topic)

Topic: Migraine and Headache Studies (Psychiatry and Mental health, Medicine), according to OpenAlex

Funding: National Natural Science Foundation of China (82505235); Beijing University of Chinese Medicine (90011451310085)

Citations: not cited yet (Europe PMC); 47 references in the paper

Abstract

Metabolic syndrome (MetS) has been associated with migraine, but the prediagnostic phase and the molecular features preceding migraine onset remain unclear. We aimed to identify a diagnosis-anchored proteomic variability window and to characterize pathways, candidate bridge proteins, and druggable targets within a direction-consistent MetS-to-migraine molecular framework. We first assessed the association between baseline MetS and incident migraine in 452,471 UK Biobank participants using Cox models. We then conducted a nested proteomics case–control analysis stratified by MetS status and applied single-sample Jensen–Shannon divergence (sJSD), network proximity, directed diffusion, and drug–target proximity analyses. Baseline MetS was associated with a higher risk of incident migraine (hazard ratio 1.09, 95% confidence interval (CI) 1.01–1.18; p = 0.022). A diagnosis-anchored proteomic divergence pattern peaked 4.71–6.76 years before migraine diagnosis in the MetS stratum. The parallel within-stratum analysis in the NoMetS stratum showed no T2-centered peak. We identified 11 direction-consistent novel pathways, seven candidate bridge proteins spanning metabolic, endothelial, and brain tissues, and three candidate drugs. These findings support a diagnosis-stratified framework for studying MetS-related migraine and provide testable hypotheses for future mechanistic and pharmacological evaluation.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

ukbiobank.ac.uk/enable-your-research, at UK Biobank; found in “Data Availability Statement”

Data Availability Statement

Restrictions apply to the availability of these data. Data were obtained from UK Biobank and are available at https://www.ukbiobank.ac.uk/enable-your-research (accessed on 18 September 2025) with the permission of UK Biobank.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 7 keywords, 9 MeSH terms, 2 funders, 47 references.

Cite

This paper

Wang, B., Li, Y., Liu, Y., & Han, D. (2026). Integrating Divergence-Based Proteomic Analysis and Directed Network Diffusion to Characterize Diagnosis-Anchored Molecular Variability at the Metabolic Syndrome-Migraine Interface. International journal of molecular sciences, 27(11), 4820. https://doi.org/10.3390/ijms27114820

BibTeX

@article{wang2026integrating, author = {Wang, Bei and Li, Yulin and Liu, Yixing and Han, Dongran}, title = {{Integrating Divergence-Based Proteomic Analysis and Directed Network Diffusion to Characterize Diagnosis-Anchored Molecular Variability at the Metabolic Syndrome-Migraine Interface}}, journal = {International journal of molecular sciences}, year = {2026}, month = may, volume = {27}, number = {11}, pages = {4820}, publisher = {Multidisciplinary Digital Publishing Institute (MDPI)}, issn = {1422-0067}, doi = {10.3390/ijms27114820}, url = {https://doi.org/10.3390/ijms27114820}, pmid = {42278351}, pmcid = {PMC13257391} }

RIS

TY - JOUR AU - Wang, Bei AU - Li, Yulin AU - Liu, Yixing AU - Han, Dongran TI - Integrating Divergence-Based Proteomic Analysis and Directed Network Diffusion to Characterize Diagnosis-Anchored Molecular Variability at the Metabolic Syndrome-Migraine Interface T2 - International journal of molecular sciences J2 - Int J Mol Sci PY - 2026 DA - 2026/05/27 VL - 27 IS - 11 SP - 4820 SN - 1422-0067 PB - Multidisciplinary Digital Publishing Institute (MDPI) DO - 10.3390/ijms27114820 UR - https://doi.org/10.3390/ijms27114820 LA - en ER -

CSL-JSON

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