Physical function‐derived cut‐points for the diagnosis of sarcopenia and dynapenia from the Canadian longitudinal study on aging
Bibliographic record
Abstract
Abstract Background Aging is associated with sarcopenia (low muscle mass) and dynapenia (low muscle strength) leading to disability and mortality. Widely used previous cut‐points for sarcopenia were established from dated, small, or pooled cohorts. We aimed to identify cut‐points of low strength as a determinant of impaired physical performance and cut‐points of low appendicular lean mass (ALM) as a predictor of low strength in a single, large, and contemporary cohort of community‐dwelling older adults and compare these criteria with others. Methods Cross‐sectional analyses were conducted on baseline data from 4725 and 4363 community‐dwelling men and women (65–86 years, 96.8% Caucasian) of the Canadian longitudinal study on aging comprehensive cohort. Physical performance was evaluated from gait speed, timed up‐and‐go, chair rise, and balance tests; a weighted‐sum score was computed using factor analysis. Strength was measured by handgrip dynamometry; ALM, by dual‐energy X‐ray absorptiometry and ALM index (ALMI; kg/m2), was calculated. Classification and regression tree analyses determined optimal sex‐specific cut‐points of ALMI predicting low strength and of strength predicting impaired physical performance (score < 1.5 SD below the sex‐specific mean). Results Modest associations were found between ALMI and strength and between strength and physical performance score in both sexes. ALMI was not an independent predictor of physical performance score. Cut‐points of <33.1 and <20.4 kg were found to define dynapenia in men and in women, respectively, corresponding to 21.5% and 24.0% prevalence rates. Sarcopenia cut‐points were <7.76 kg/m2 in men and <5.72 kg/m2 in women; prevalence rates of 21.7% and 13.7%. Overall, 8.3% of men and 5.5% of women had sarco‐dynapenia. Sarcopenic were older and had lower fat mass and body mass index (BMI) than non‐sarcopenic participants. While the agreement between current criteria and the updated European Working Group for Sarcopenia in Older Persons recommendations was fair, we found only slight agreement with the Foundation for the National Institute of Health sarcopenia project. Older persons identified with sarcopenia as per the Foundation for the National Institute of Health criteria (using ALM/BMI as the index) have higher BMI and fat mass compared with non‐sarcopenic and have normal ALMI as per our criteria. Conclusions The proposed function‐derived cut‐points established from this single, large, and contemporary Canadian cohort should be used for the identification of sarcopenia and dynapenia in Caucasian older adults. We advise on using criteria based on ALMI in the diagnosis of sarcopenia. The modest agreement between sarcopenia and dynapenia denotes potential distinct health implications justifying to study both components separately.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".