Inflammatory biomarkers and motoric cognitive risk syndrome: Multicohort survey
Bibliographic record
Abstract
Background: Inflammation may play a role in Motoric Cognitive Risk (MCR) syndrome, a pre-dementia syndrome comprised of slow gait and cognitive complaints. Our objective was to examine associations of inflammatory biomarkers with MCR. Methods: We examined association of interleukin-6 (IL-6) and C-reactive protein (CRP) with prevalent MCR using logistic regression in 3,101 older adults (52% female) from five cohorts (National Center for Geriatrics & Gerontology Study of Geriatric Syndromes [NCGG-SGS], Central Control of Mobility in Aging [CCMA], Tasmanian Study of Cognition and Gait [TASCOG], LonGenity, and Einstein Aging Study [EAS]). Associations were reported as odds ratios adjusted for sex, age, education, depressive symptoms, body mass index, and vascular diseases (aOR) with 95% confidence intervals (CI). Meta-analysis and analyses stratified by vascular disease were also done. Results: Although associations between higher (worse) CRP and IL-6 tertiles and MCR were only seen in three out of the five cohorts (EAS, TASCOG, and LonGenity), when a pooled meta-analysis was performed, a robust association was demonstrated. In meta-analysis, highest tertiles of IL-6 (aOR 1.57, 95%CI 1.01- 2.44) and CRP (aOR 1.65, 95%CI 1.09-2.48) was associated with MCR versus lowest tertiles in the pooled sample. Higher CRP was associated with MCR among those with vascular disease in TASCOG and LonGenity cohorts, and among those without vascular disease in EAS. Conclusions: IL-6 and CRP levels are associated with MCR in older adults, and this association varies by presence of vascular disease.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".