<scp>DNA</scp> Methylation Signatures of Systemic Inflammation Are Associated With Brain Volume, Cognitive Trajectories, and Long‐Term Dementia Risk
Bibliographic record
Abstract
C-reactive protein (CRP) and growth differentiation factor 15 (GDF15) are important markers of inflammation associated with brain health. Compared to plasma, DNA methylation (DNAm) measures of CRP and GDF15 may provide stable epigenetic measures of chronic exposure to inflammation and could therefore be robustly predictive of inflammation-related brain aging and neurodegeneration. We leveraged a subsample of Baltimore Longitudinal Study of Aging (BLSA) participants with DNAm/plasma data and longitudinal neuroimaging/cognition data (n = 430-1100). We used a proteome-wide analysis to characterize the biology of DNAm CRP and GDF15, and latent growth curve models to explore the associations with longitudinal trajectories of 19 brain region volumes and five cognitive domains. Finally, we related DNAm/plasma CRP and GDF15 to dementia risk in two external cohorts. DNAm CRP and GDF15 showed a proteomic signature consistent with systemic immune activation. We identified several brain regions with significant associations between elevated DNAm CRP And GDF15 and (a) lower brain volume level (at age 75) and (b) greater rate of atrophy. Compared to plasma CRP, DNAm CRP was more strongly associated with brain volume, cognitive trajectories, and dementia risk. DNAm and plasma GDF15 were similarly associated with several total lobar, total lobar white matter, and AD-relevant region trajectories and dementia risk, but DNAm measures outperformed plasma measures in relation to cognitive trajectories. Epigenetic signatures of CRP and GDF15 reflect immune and inflammation-related pathway activation. These signatures, especially DNAm CRP, were associated with accelerated brain atrophy, cognitive decline, as well as long-term dementia risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".