Are the Healthy Vulnerable? Cytomegalovirus Seropositivity in Healthy Adults Is Associated With Accelerated Epigenetic Age and Immune Dysregulation
Bibliographic record
Abstract
BACKGROUND: Evaluating age as a risk factor for susceptibility to infectious diseases, particularly coronavirus disease 2019 (COVID-19), is critical. Cytomegalovirus (CMV) serologic prevalence increases with age and associates with inflammatory-mediated diseases in the elderly. However, little is known regarding the subclinical impact of CMV and risk it poses to healthy older adults. Prior to the COVID-19 pandemic we conducted a study to determine the association of CMV to biologic age and immune dysregulation. METHODS: Community-dwelling, healthy adults older than 60 years were evaluated using DNA methylation assays to define epigenetic age (EpiAge) and T-cell immunophenotyping to assess immune dysregulation. RESULTS: All subjects were healthy and asymptomatic. Those CMV seropositive had more lymphocytes, CD8 T cells, CD28- T cells, decreased CD4:CD8 cell ratios, and had higher average EpiAge (65.34 years) than those CMV seronegative (59.53 years). Decreased percent CD4 (P = .003) and numbers of CD4 T cells (P = .0199) correlated with increased EpiAge. CONCLUSIONS: Our novel findings distinguish altered immunity in the elderly based on CMV status. Chronic CMV infection in healthy, older adults is associated with indicators of immune dysregulation, both of which correlate to differences in EpiAge.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 0.000 |
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.
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".