Inflammation and Fat Mass as Determinants of Changes in Physical Capacity and Mobility in Older Adults Displaying A Large Variability in Body Composition: The NuAge Study
Bibliographic record
Abstract
Background/Study context: Determining whether C-reactive protein (CRP), blood lipids, total and trunk fat mass (FM), and waist circumference (WC) are associated with changes in physical capacity over 3 years (Δ) in elderly. METHODS: One hundred twenty-two men and women 68-83 years of age participated in a 3-year follow-up study. Physical capacity was measured using five objective tests: (1) Timed Up and Go (TUG), (2) chair stand (CS), (3) normal walking speed (NWS), (4) fast walking speed (FWS), and (5) one-leg stand (LS), along with physical performance score (PPS) at baseline (T1) and 3 years later (T4). Total and trunk FM, WC, blood lipids, and CRP measured at baseline, were considered as potential predictors. RESULTS: At baseline, CRP and total FM were significantly correlated with all physical capacity tests, whereas trunk FM was correlated with CS and LS, and blood lipids only with FSW. No significant correlation was observed for WC. Total and trunk FM measured at baseline were correlated with ΔTUG and ΔPPS, whereas trunk FM and WC measured at baseline were correlated with ΔNWS. CRP and blood lipids, measured at baseline, were not associated with any changes over 3 years. At the end, WC measured at baseline was the strongest independent predictor for all physical capacity measures at baseline (T1), and ΔPPS measured over 3 years could be predicted by baseline WC. CONCLUSION: FM distribution seems more useful to determine physical capacity than inflammation. Interestingly, over a short follow-up of 3 years, WC significantly predicted changes in a composite score of physical activity. More studies are needed to elucidate factors that may influence physical capacity decline over time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".