Aftereffects in Epigenetic Age Related to Cognitive Decline and Inflammatory Markers in Healthcare Personnel with Post-COVID-19: A Cross-Sectional Study
Bibliographic record
Abstract
Purpose: Epigenetic age and inflammatory markers have been proposed as indicators of severity and mortality in patients with COVID-19. Furthermore, they have been associated with the occurrence of neurological symptoms, psychiatric manifestations, and cognitive impairment. Therefore, we aimed to explore the possible associations between epigenetic age, neuropsychiatric manifestations and inflammatory markers (neutrophil-lymphocyte ratio [NLR], platelet-lymphocyte ratio [PLR], monocyte-lymphocyte ratio [MLR], and systemic immune-inflammation index [SII]) in healthcare personnel with post-COVID condition. Patients and Methods: We applied the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) tests to 51 Mexican healthcare workers with post-COVID-19 condition; we also estimated their epigenetic age using the PhenoAge calculator. Results: The participants had a post-COVID condition that lasted a median of 14 months (range: 1-20). High NLR (>1.73) had association with mild cognitive impairment by MMSE (p=0.013). Likewise, high MLR (>0.24) were associated with language domain in MOCA (p=0.046). Low PLR (<103.9) was also related to delayed recall in MOCA (p=0.040). Regarding comorbidities, hypertension was associated with SII (p=0.007), overweight with PLR (p=0.047) and alcoholism was associated with MLR (p=0.043). Interestingly, we observed associations of low PLR (<103.9) and low SII (<1.35) levels with increased duration of post-COVID condition (p=0.027, p=0.031). Likewise, increases in PhenoAge were associated with high levels of SII (OR=1.11, p=0.049), PLR (OR=1.12, p=0.035) and MLR (OR=1.12, p=0.030). Conclusion: We observed neurocognitive changes related to inflammatory markers and increases in epigenetic age in healthcare personnel with post-COVID-19 condition. Future research is required to assess mental and physical health in individuals with post-COVID-19 symptoms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".