Association of Motoric Cognitive Risk Syndrome with Sarcopenia and Systemic Inflammation in Pre-Frail Older Adults
Bibliographic record
Abstract
Motoric cognitive risk syndrome (MCR) is defined by the presence of slow gait and subjective cognitive decline. It is well recognized as a prodrome for dementia, but the biological mechanism and trajectory for MCR are still lacking. The objective of this study was to explore the association of MCR with body composition, including sarcopenia and systemic inflammation, in pre-frail older adults in a cross-sectional study of 397 pre-frail community-dwelling older adults. Data on demographics, physical function, frailty, cognition (Montreal Cognitive Assessment (MoCA)), perceived health and depression were collected. Body composition was measured using a bioelectrical impedance analyzer. Systemic inflammatory biomarkers, such as progranulin, growth differentiation factor-15 (GDF-15), interleukin-10 (IL-10), interleukin-6 and tumor necrosis factor-α (TNF-α), were collected. Univariate and multivariate logistic regression were used to analyze the association between MCR, body composition, sarcopenia and systemic inflammatory biomarkers. The prevalence of MCR was 14.9%. They were significantly older and there were more females, depression, functional impairment, lower education, physical activity and MoCA scores. Body fat percentage (BF%), fat mass index, fat to fat free mass ratio (FM/FFM) and sarcopenia prevalence were significantly higher in MCR. Serum GDF-15 and TNF-α levels were highest with progranulin/TNF-α and IL-10/TNF-α ratio lowest in MCR. Compared to healthy patients, MCR was significantly associated with sarcopenia (aOR 2.62; 95% CI 1.46-3.17), BF% (aOR 1.06; 95% CI 1.01-1.12), FMI (aOR 1.16; 95% CI 1.02-1.30) and FM/FFM (aOR 6.38; 95% CI 1.20-33.98). The association of IL-10 to TNF-α ratio (aOR 0.98, 95% CI 0.97-0.99) and IL-10 (aOR 2.22, 95% CI 0.05-0.98) with MCR were independent of sarcopenia and BF%. Longitudinal population studies are needed to understand the role of body fat indices and IL-10 in pre-frail older adults with MCR and trajectory to dementia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".