Higher Muscle Volume is Inversely Related to Chronological and Brain Age While Increased Visceral to Muscle Fat Ratio is Positively Related to Chronological and Brain Age
Bibliographic record
Abstract
Abstract Background Brain age predicted from structural brain images on T1 weighted scans can lend insight to Alzheimer's risk factors such as muscle loss with sarcopenia. We thus investigated the link between body MRI measured muscle mass, muscle to fat ratio and brain age. Method In all, 1,164 healthy participants from four sites (mean chronological age 55.17 ± 12.37 years, 52% women; 48% men; 39% non‐white) were scanned on 1.5T MR machines with a whole‐body protocol. Whole body sequences utilized in the quantitative analyses of muscle mass were coronal T1 were used to segment total muscle volume normalized to participant height, visceral adipose tissue (VAT) and subcutaneous adipose tissue (VAT). In this process, a nnU‐Net model was used for fully supervised segmentation and ITK‐SNAP was used for manual annotation. Brain age was computed from T1 MPRAGE scans using a regression‐based 3D Simple Fully Convolutional Network. The model was trained on in‐house T1‐weighted MRI scans collected from 5,500 healthy individuals, aged 18 to 89 years. Brain age gap (BAG) was calculated by subtracting chronological age from brain age. Bivariate correlations between total normalized muscle volume (TNMV) as well as VAT and SAT normalized to total muscle volume to chronological and brain age were done with partial correlations adjusted for sex with brain age analyses. Result Mean brain age was higher than chronological age (56.04 ± 12.65, mean BAG = 0.69). Higher TNMV was related to both decreased chronological age (rp=‐0.2579, p = 2.524e‐17) and brain age (rp =‐0.2497, p = 2.65e‐16). VAT normalized to total muscle volume was linked to higher chronological (rp=0.3755, p = 2.615e‐36) and brain age (rp=0.3797, p = 3.871e‐37) adjusting for sex. No statistically significant links were noted with TNMV, VAT, SAT or and BAG. SAT was also not correlated in a statistically significant way to chronological or brain age. Conclusion Increasing muscle mass is related to lower chronological and brain age while visceral fat normalized to muscle volume is related to increased chronological and brain age. Lack of correlation to BAG may be due to the relatively low BAG in this sample. This work suggests improving muscle mass and reducing visceral fat may improve 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 | 0.000 | 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) | 0.004 | 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".