Comparison of cerebrospinal fluid biomarkers in patients with severe COVID-19 neurological outcomes and Alzheimer’s disease
Bibliographic record
Abstract
Background: COVID-19 induces acute and long-term neurological symptoms. Links between COVID-19 neurological disturbance and Alzheimer's disease (AD) have been hypothesized because neuroinflammation plays a significant role in both diseases. However, it is unknown if COVID-19 patients with neurological disturbance present molecular alterations related to AD pathology. A better understanding of possible molecular links between COVID-19-induced neurological disease and AD would lead to improved patient follow-up and late-onset disease prevention. Here, we analyze early AD biomarkers in a Brazilian cohort of COVID-19 patients with neurological symptoms. We compared COVID-19 patients' neuroinflammatory and AD biomarker levels to controls, amnestic mild cognitive impairment (aMCI), and AD. Methods: We analyzed cerebrospinal (CSF) biomarkers of neuroinflammation (interleukin-6 (IL6)), amyloid-beta (Aβ) proteinopathy (Aβ42/40), phosphorylated Tau (pTau181), and the neurodegeneration-associated biomarker total Tau in controls (n = 36), COVID-19 patients presenting neurological alterations (n = 35), aMCI (n = 19), and AD patients (n = 20). Comparisons were corrected by possible sex, age, and comorbidities confounding effects. CSF biomarkers were correlated with systemic and neuro-inflammation markers. Results: We found that severe COVID-19 patients presented higher CSF Tau than controls, comparable to alterations observed in AD patients. However, we did not find changes in CSF Aβ42/40, pTau-181/Aβ42, or Tau/Aβ42 ratios. Severe COVID-19 patients presented higher Tau, Tau/Aβ42, and pTau181/Aβ42 than mild patients. In COVID-19 patients, CSF pro-inflammatory cytokine IL6 and AD biomarkers correlated with systemic inflammatory index (SII). Conclusions: Collectively, our findings reveal that CSF tau levels are comparably elevated in COVID-19 neurological patients and AD, suggesting ongoing neurodegeneration in COVID-19 neurological disease, but no biomarker alterations related to AD pathology. Furthermore, CNS AD-related biomarker levels in COVID-19 patients change in association with disease severity and systemic inflammation. Considering that inflammation may persist post-COVID, our findings urge the assessment of possible AD-related biomarker changes in COVID-19 survivors with lingering symptoms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".