Body fat and components of sarcopenia relate to inflammation, brain volume, and neurometabolism in older adults
Bibliographic record
Abstract
Obesity and sarcopenia are associated with cognitive impairments at older age. Current research suggests that blood biomarkers may mediate this body-brain crosstalk, altering neurometabolism and brain structure eventually resulting in cognitive performance changes. Seventy-four older adults (60–85 years old) underwent bio-impedance body composition analysis, handgrip strength measurements, 8-Foot Up-and-Go (8UG) test, Montreal Cognitive Assessment (MoCA), blood analysis of interleukin-6 (IL-6), kynurenine, and insulin-like growth factor-1 (IGF-1), as well as brain magnetic resonance imaging (MRI) and proton magnetic resonance spectroscopy (1H-MRS), estimating neurodegeneration and neuroinflammation. Normal fat% or overweight was associated with larger total gray matter volume compared to underweight or obesity in older adults and obesity was associated with higher N-acetylaspartate/Creatine levels in the sensorimotor and dorsolateral prefrontal cortex. Muscle strength, not muscle mass/physical performance, corresponded to lower kynurenine and higher N-acetylaspartate/Creatine levels in the dorsal posterior cingulate and dorsolateral prefrontal cortex. The inflammatory and neurotrophic blood biomarkers did not significantly mediate these body-brain associations. This study used a multimodal approach to comprehensively assess the proposed mechanism of body-brain crosstalk.
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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 | 0.000 |
| 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".