Peripheral inflammation in a Canadian cohort of neurodegenerative conditions: Occurrence, determinants, and impact
Bibliographic record
Abstract
Background"Inflammaging" describes chronic low-grade inflammation observed in aging individuals. It may play a major role in neurodegeneration.ObjectiveTo assess blood inflammatory markers in older adults. We hypothesized that elevated inflammation would be found in some cognitively normal older adults but would be more prevalent in individuals with cognitive impairment.MethodsInterleukin-6 (IL-6) and C-reactive protein (CRP) were assessed in 514 Canadian individuals in COMPASS-ND, a detailed study of cognitive impairment in the elderly. Cumulative link model (CLM) was used to investigate the relationship between inflammation status (low, medium, or high tertiles) and demographic and lifestyle factors along with cognitive function and cognitive diagnoses.ResultsWe found that 12% of cognitively normal older adults had IL-6 levels in the highest tertile, but this increased in cognitively impaired cohorts-36% in Alzheimer's disease, 55% mixed dementia, 30% mild cognitive impairment, and 39% vascular mild cognitive impairment. We found that 36% of cognitively unimpaired older individuals display "elevated" IL-6 (middle and high tertile values), while approximately 70% of those with cognitive impairment also do so. Inflammation markers increased most robustly in association with age, higher body mass index, and higher Fazekas (MRI white matter hyperintensity) score. There were also weaker associations with female sex, nutrition, number of comorbidities, and poor sleep.ConclusionsPeripheral low-grade inflammation was common, particularly in individuals with cognitive impairment; and obesity and age were the main drivers. It remains unclear whether treatment targeting such inflammation might have a therapeutic role in dementia prevention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".