Low Physical Function Predicts Either 2-Year Weight Loss or Weight Gain in Healthy Community-Dwelling Older Adults. The NuAge Longitudinal Study
Bibliographic record
Abstract
OBJECTIVES: Weight change in older adults affects physical function (PF). However, data suggest that, conversely, PF may be a determinant of weight change. Our objective was to assess the role of baseline PF as a predictor of 2-year weight loss (WL) and weight gain (WG) ≥ 5% among healthy well-functioning community-dwelling older adults. METHODS: The NuAge cohort (67-84 years) was classified into three groups according to the percent weight change over a 2-year follow-up: weight stable (weight change ≤ 2%; n = 629), WL ≥ 5% (n = 189), and WG ≥ 5% (n = 111). A summary measure of baseline PF was computed (sum of biceps, quadriceps, and grip strength, timed up and go, chair stand, normal and maximal gait speed, and balance performance scores [individual test score range = 0-4]; PF score range = 0-32). Multivariable logistic regression models separately assessed the relationships between baseline PF and 2-year WL and WG ≥ 5%. RESULTS: Baseline PF was worse in both the WL (p < .001) and the WG (p = .001) groups compared with the weight stable group. In models adjusting for sex, age, body mass index, energy intake, depressive symptoms, and other significantly associated covariates, each 1-unit increase in standard deviation of PF was associated with decreased risk of either 2-year WL (odds ratio = 0.79, 95% CI = 0.63-0.99, p = .043) or WG (odds ratio = 0.74, 95% CI = 0.55-0.99, p = .041). CONCLUSIONS: Low baseline PF was an independent common predictor of 2-year WL and WG ≥ 5% in the healthy well-functioning community-dwelling elderly population. Whether PF is an early cause or marker of weight change in this population remains to be determined.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".