Temporalis muscle thickness as a biomarker for sarcopenia: correlation with appendicular skeletal muscle mass in healthy older adults
Bibliographic record
Abstract
Abstract Background Sarcopenia, an age‐related loss of skeletal muscle strength, mass and function, is linked with dementia and Alzheimer's disease (AD). Current guideline‐recommended tools to diagnose sarcopenia, such as appendicular skeletal muscle mass (ASM), calculated as the sum of lean muscle mass in the arms and legs via dual‐energy X‐ray absorptiometry (DXA), are not commonly done in AD studies. However, brain magnetic resonance imaging (MRI) is regularly performed in AD studies, and temporalis muscle thickness (TMT) has been suggested as a potential sarcopenia biomarker. As a first step in evaluating whether TMT could be a useful sarcopenia diagnostic tool, we aimed to ascertain if TMT correlates with ASM in healthy older adults. Method We conducted a retrospective study of healthy cognitively‐unimpaired older adults in the Intense Physical Activity and Cognition study, in whom MRI and DXA had been performed on the same visit. TMT was measured on axial T1‐weighted MRIs bilaterally perpendicular to the long‐axis of the temporalis muscle using the orbital roof and Sylvian fissure as anatomical landmarks, and average TMT used for analysis. ASM was adjusted for body size (height 2 ). Sarcopenia was defined as ASM< 7.0 kg/m 2 for males and <5.5 kg/m 2 for females as per the 2019 European working group on sarcopenia in older people criteria. Pearson correlation assessed the relationship between TMT and ASM or age. Result There were 95 participants (mean±standard deviation [SD] age 69.1±5.2 years, 53% female, median Montreal Cognitive Assessment score 27 [Interquartile range 25 – 28],11% had sarcopenia). The mean±SD ASM was 7.0±1.2 kg/m 2 and TMT was 7.3±1.2 mm. TMT and ASM were moderately correlated ( r = 0.41, 95% confidence interval 0.23 – 0.56). TMT did not correlate with age but differed significantly between those with (mean±SD 7.4±1.2) and without sarcopenia (mean±SD 6.2±0.8, p = 0.004). Conclusion Among a cohort of cognitively‐unimpaired older adults, TMT demonstrated moderate correlation with ASM. While futher studies are needed, these findings suggest that MRI‐based assessement of TMT could be a practical tool to diagnose sarcopenia in AD studies. Future studies in AD patients should explore the relationship between TMT and long‐term clinical and functional outcomes.
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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 | 0.001 | 0.002 |
| 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".