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Record W4406763520 · doi:10.1503/jpn.240039

Association of systemic inflammatory markers with white matter hyperintensities and microstructural injury: an analysis of UK Biobank data

2025· article· en· W4406763520 on OpenAlexvenueno aff
Yuan Qiao, Lei Zhao, Chaohua Cong, Yuna Li, Shan Tian, Xirui Zhu, Junting Yang, Shanshan Cao, Panlong Li, Jingjing Su

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsnot available
FundersSchool of Medicine, Shanghai Jiao Tong UniversityShanghai Jiao Tong UniversityNational Natural Science Foundation of China
KeywordsBiobankHyperintensityAssociation (psychology)MedicineLeukoaraiosisPsychologyBiologyBioinformaticsMagnetic resonance imaging

Abstract

fetched live from OpenAlex

BACKGROUND: White matter damage is closely associated with cognitive and psychiatric symptoms and is prevalent in cerebral small vessel disease (CSVD); although the pathophysiological mechanisms involved in CSVD remain elusive, inflammation plays a crucial role. We sought to investigate the relationship between systemic inflammation markers and imaging markers of CVSD, namely white matter hyperintensity (WMH) and microstructural injury. METHODS: We conducted a study involving both cross-sectional and longitudinal data from the UK Biobank Cohort. We performed multiple linear regression analyses, adjusted for potential confounders, to explore the associations between systemic inflammation markers (e.g., systemic immune-inflammation index [SII], neutrophil-to-lymphocyte ratio [NLR], C-reactive protein [CRP] levels, monocyte count, neutrophil count) and macro- and microstructural white matter injury, as markers of CSVD. We performed Mendelian randomization analysis to investigate the genetically predictive effect of monocytes on WMH, as well as mediation analysis to clarify whether inflammatory markers affected cognitive function via white matter injury. RESULTS: We included 36 411 participants (mean age 54.8 ± 7.5 yr, 51.9% female) from the UK Biobank Cohort. We found that SII was significantly associated with both WMH and microstructural injury markers (fractional anisotropy, mean diffusivity, intracellular volume fraction, and isotropic compartment volume fraction [ISOVF]), and the neutrophil-to-lymphocyte ratio was significantly associated with WMH and some markers of microstructural injury (mean diffusivity and ISOVF). Our analysis revealed that the CRP level was significantly associated with WMH and WMH progression but not with microstructural injury. We also demonstrated that monocyte count was significantly associated with WMH and ISOVF, and that neutrophil count was significantly associated with WMH, mean diffusivity, and ISOVF. In 2-sample Mendelian randomization analyses, we found positive associations between genetic determinants of monocytes and WMH. The mediating role of WMH suggested that a higher SII value and monocyte count could contribute to cognitive impairment through white matter injury. LIMITATIONS: Although the study includes both cross-sectional and longitudinal components, the sample size for the longitudinal aspect is limited, and the use of blood biomarkers from a single timepoint is also a limitation of this research. CONCLUSION: The SII and neutrophil-to-lymphocyte ratio may be early detection markers for white matter damage in patients with CSVD, whereas the CRP level is more closely associated with disease severity and progression. Our findings highlight the clinical relevance of systemic inflammation markers with white matter macro- and microstructural injuries, revealing that systemic inflammation is likely involved in the mechanism of early white matter injury among patients with CSVD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.257
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2025
Admission routes1
Has abstractyes

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