Fruit and Vegetable Consumption and Sarcopenia among Older Adults in Low- and Middle-Income Countries
Bibliographic record
Abstract
Fruit and vegetable consumption may protect against sarcopenia but there are no studies on this topic from low- and middle-income countries (LMICs). Thus, we assessed this association among older adults from six LMICs. Community-based cross-sectional data of the Study on Global Aging and Adult Health were analyzed. Sarcopenia was defined as the presence of low skeletal muscle mass based on indirect population formula, and either slow gait or low handgrip strength. Quintiles of vegetable and fruit consumption were created based on the number of servings consumed on a typical day. Multivariable logistic regression analysis was conducted. The sample consisted of 14,585 individuals aged ≥65 years (mean (SD) age 72.6 (11.4) years; 55% females). Adjusted analyses showed that overall, compared to the lowest quintile (Q1), the highest quintile (Q5) of fruit consumption was associated with a 40% lower odds for sarcopenia (OR = 0.60; 95% CI = 0.42-0.84) but this association was largely driven by the strong association among females (e.g., Q5 vs. Q1 OR = 0.42; 95% CI = 0.24-0.73), with no significant associations found among males. Vegetable consumption was not significantly associated with sarcopenia. Future studies of longitudinal design may shed light on whether increasing fruit consumption among older females in LMICs may reduce risk for sarcopenia.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".