Fat Mass But Not Fat-Free Mass Is Related to Physical Capacity in Well-Functioning Older Individuals: Nutrition as a Determinant of Successful Aging (NuAge)--The Quebec Longitudinal Study
Bibliographic record
Abstract
BACKGROUND: Aging is associated with increases in fat mass (FM) and decreases in fat-free mass (FFM) that may affect physical capacity. However, it is not clear whether high FM or low FFM contribute more to a reduction in physical capacity. METHODS: A structural equation modeling strategy was used to test an explanatory model of the association between body composition and physical capacity. The concept of physical capacity was assessed by walking speed at a normal pace and the one leg stand test. To test the model, 904 men and women between 67 and 84 years old were studied. Body composition was measured by dual-energy x-ray absorptiometry (DXA). Confounding factors related to body composition and physical capacities were included in the explanatory model (physical activity level, age, gender, and number of reported diseases). RESULTS: The final model showed that physical capacity can be represented by a factorial first-order model including generic measures of walking speed and balance. Moreover, our results showed that percentage of FM was significantly associated with physical capacity (p<.01), whereas no such association was observed with FFM. Other variables such as physical activity level, number of self-reported diseases, and age were associated with physical capacity (all p<.01). Overall, the proposed model explained 48% and 57% of the variance observed in men and women when using the one leg stand and the walking speed at normal pace tests as measures of physical capacity. CONCLUSION: FM was significantly and inversely correlated with physical capacity, whereas FFM was not associated when controlled for other potential confounding variables. More studies are needed to investigate the effect of different levels of obesity on physical capacity in older individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".