When Do We Start Observing A Decline In Muscle Strength And Mass As We Age?
Bibliographic record
Abstract
Sarcopenia is a common condition in older adults, and it occurs after years of muscle strength and muscle mass loss. However, the age at which these two variables start to decline remains elusive. PURPOSE: To determine the age when muscle strength and mass reach a peak score and start to decline, as well as their average decline rate by sex in young and middle-aged adults. METHODS: This was a secondary data analysis from the cross-sectional National Health and Nutrition Examination Survey (NHANES), cycles 2011-2014. We analyzed the registries from 2562 females and 2746 males (ages 20 to 59 y) with data of combined handgrip strength (HGS) as a measure of muscle strength, DXA-derived appendicular lean soft tissue (ALST), and legs and arms lean soft tissue (LST), as indicators of muscle mass. Individual segmented linear regression models were used with age as a predictor (continuous in years) and the HGS (kg force), ALST (kg), legs LST (kg), and arms LST (kg) as outcome variables to determine the age when the outcome variable reached a peak score (age breaking point) and posterior slope. RESULTS: The mean ± SD for age, HGS, ALST, legs and arms LST was 38.9 ± 11.4 y, 59.2 ± 10.6 kg, 18.5 ± 4.4 kg, 14.0 ± 3.5 kg, and 4.6 ± 1.1 kg, for females, and 38.2 ± 11.5 y, 92.5 ± 16.9 kg, 27.1 ± 5.2 kg, 19.3 ± 3.8 kg, 7.8 ± 1.5 kg, for males, respectively. The HGS decline started at similar ages in males (34.9 y, 95% CI: 32.5 - 37.3) and females (33.0 y, 30.1 - 35.9), with a more pronounced decline in males (-1.03 kg/y) than females (-0.61 kg/y). However, the ALST decline started later in females (48.5 y, 43.5 - 53.4) than in males (35.9 y, 30.8 - 41.0) with similar decline rates (-0.16 kg/y both). The arms LST decline started at similar ages in males (36.1 y, 32.7 - 39.4) and females (33.0 y, 27.5 - 38.5), but males showed a more pronounced decline than females (-0.07 vs -0.03 kg/y, respectively). Finally, the starting decline in legs LST was observed later in females (48.9 y, 44.2 - 53.6) than in males (35.4 y, 28.9 - 42.0), with a greater decline in females than males (-0.13 vs -0.09 kg/y, respectively). CONCLUSIONS: The age when muscle strength started to decline was similar by sex (about 34 y) but differs in the decline rate. The age when muscle mass started to decline was sex-dependent and occurs later in females mainly because of the later start in legs muscle mass decline.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".