Association of Chronic Pain with Biomarkers of Neurodegeneration, Microglial Activation, and Inflammation in Cerebrospinal Fluid and Impaired Cognitive Function
Bibliographic record
Abstract
Debate surrounds the role of chronic pain as a risk factor for cognitive decline and dementia. This study aimed at examining the association of chronic pain with biomarkers of neurodegeneration using data from the Alzheimer's Disease Neuroimaging Initiative.MethodsParticipants were classified using the ATN (amyloid, tau, neurodegeneration) classification. Chronic pain was defined as persistent or recurrent pain reported at baseline. For each ATN group, analysis of covariance models identified differences in cerebrospinal fluid (CSF) levels of amyloid β1–42, phosphorylated tau 181 (ptau181), total tau (t-tau), soluble triggering receptor expressed on myeloid cells 2 (sTREM2), and cognitive function between chronic pain states. Differences in CSF levels of inflammatory markers between chronic pain states were further analyzed. Linear mixed effect models examined longitudinal changes.ResultsThe study included 995 individuals, with 605 (60.81%) reporting chronic pain at baseline. At baseline, individuals with suspected non-Alzheimer pathophysiology and chronic pain showed increased CSF levels of t-tau and sTREM2. Chronic pain was associated with increased tumor necrosis factor α levels, irrespective of the ATN group. Longitudinally, an increase in ptau181 CSF levels was observed in chronic pain patients with negative amyloid and neurodegeneration markers. Amyloid-positive and neurodegeneration-negative chronic pain patients showed higher memory function cross-sectionally. No significant longitudinal decline in cognitive function was observed for any ATN group.InterpretationOur study suggests that chronic pain induces neuronal damage and microglial activation in particular subgroups of patients along the AD spectrum. Further studies are needed to confirm these findings.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".