Mediating role of muscle quality in the liver–brain axis: integrated analysis of CT markers of body composition, brain aging, and biomarkers
Bibliographic record
Abstract
Background The liver, skeletal muscle, and brain are interconnected through metabolic and endocrine pathways, constituting a systemic axis that may influence neurodegeneration. Although hepatic steatosis and sarcopenia have been independently associated with neurodegeneration, their integrated effects on the brain remain poorly understood. This study investigated whether muscle density mediated the link between hepatic steatosis and neurodegeneration, quantified via the brain age gap (BAG). Methods Data from 2,510 adults (aged 22–87 years) who underwent abdominal computed tomography (CT), brain magnetic resonance imaging (MRI), and blood tests during comprehensive health evaluations were retrospectively analyzed. Fully automated CT markers, including visceral and subcutaneous fat, muscle, and the liver attenuation index (LAI) (a CT-based surrogate of hepatic steatosis) were obtained. The BAG was calculated from T1-weighted structural MRI scans using a pretrained machine learning pipeline. Mediation analysis was performed to evaluate the indirect effects of LAI on the BAG through muscle density. Network analysis further characterized the multivariate associations between the BAG, CT markers of body composition, laboratory results, and anthropometric variables. Results Mediation analysis confirmed that muscle density, not muscle volume, partially mediated the LAI–BAG relationship (indirect β = −0.04, p < 0.001). LAI was negatively associated with the BAG (β = −0.027, p = 0.002) and positively associated with muscle density (β = 0.049, p < 0.001), whereas muscle density was inversely associated with BAG (β = −0.080, p < 0.001). Network analysis identified muscle density as a central hub linking the LAI, body composition, and the BAG. The BAG was also negatively correlated with Montreal Cognitive Assessment scores ( r = −0.20, p < 0.001). Conclusion Muscle density mediates the effect of hepatic steatosis on brain aging, supporting its role as a key modifiable factor within the liver–brain axis. These findings underscore the importance of preserving muscle quality to decelerate brain aging.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".