Epigenetic biomarkers of inflammation in studies on mental health: Current applications, limitations, and future directions
Bibliographic record
Abstract
• Epigenetic biomarkers of inflammation may advance immune-mental health research. • 29 EWAS studies of inflammatory proteins provide source data for DNA methylation (DNAm) biomarkers. • 14 studies examined DNAm markers of inflammatory proteins (mostly CRP or IL-6) in relation to mental health, neuroimaging measures and cognition. • Training data characteristics strongly impact DNAm marker performance and interpretation. • Further research is warranted on epigenetic biomarkers of inflammation for prediction and potentially mechanistic insights in mental health. Inflammation has emerged as a potentially modifiable risk factor for mental illness, warranting further research. Epidemiological studies have primarily relied on measuring blood levels of single inflammatory proteins, but these have proven limited in detecting robust long-term associations with mental health. DNA methylation (DNAm)-based epigenetic biomarkers of inflammation have demonstrated greater predictive performance than protein markers of several physical health outcomes and hold potential to increase understanding of the immune-mental health link. We conducted a comprehensive literature review of mental health studies using DNAm markers of inflammation. First, PubMed and EMBASE were searched for epigenome-wide association studies (EWAS) of inflammation, providing source data for deriving DNAm biomarkers. Second, we identified studies that used DNAm markers of inflammation to study mental health-related outcomes. We address technical considerations, biological implications, and future research directions. We identified 29 EWAS studies of circulating inflammatory proteins, of which C-reactive protein (CRP) was the most frequently studied. In addition, 14 studies examined epigenetic biomarkers of inflammation in relation to mental health, cognition or neuroimaging outcomes, where higher levels of DNA methylation (DNAm) markers of CRP and interleukin-6 (IL-6) were consistently associated with reduced cognitive function, lower brain volumes, and reduced white matter integrity. In contrast, findings for mental health (most often studying depression) were less conclusive, with studies reporting either positive or null associations. Epigenetic biomarkers of inflammation are emerging as valuable tools in mental health research. Further study on the value of these markers for mental health prediction is warranted, evaluating shared and distinct relations across mental illnesses. Advances may also come from developing novel epigenetic biomarkers that capture inflammatory processes with greater resolution, and from examining altered inflammatory states in relation to mental health risk exposures across the lifecourse.
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 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 | 0.249 | 0.281 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".