Associations Between Poor Appetite, Sarcopenia, and Cognitive Function in Community-dwelling Malaysian Older Adults
Bibliographic record
Abstract
OBJECTIVES: This study aimed to explore the associations between poor appetite, sarcopenia, and cognitive function among Malaysian older adults. METHODS: This nationwide study involved 1086 community-dwelling older adults aged 55 years and above. Poor appetite was defined using a self-reported question, while cognitive function was assessed with the Montreal Cognitive Assessment. Sarcopenia was identified based on handgrip strength, 6-meter gait speed, and muscle mass, in accordance with the Asian Working Group for Sarcopenia 2019 criteria. Associations between poor appetite, sarcopenia, and cognitive function were analyzed using both univariate and multivariate analyses. RESULTS: Multivariate analysis revealed that handgrip strength (β=0.067, p=0.012) and gait speed (β=1.080, p=0.017) were significantly associated with cognitive function after adjusting for confounders such as age, ethnicity, marital status, education, and alcohol and smoking consumption. However, no significant association was observed between poor appetite and cognitive function, nor was any moderation effect found between poor appetite and sarcopenia-related traits on cognitive function. CONCLUSIONS: Our study confirms that low muscle strength and reduced physical performance are significantly associated with an increased risk of cognitive impairment among community-dwelling older adults. These findings underscore the critical importance of muscle strength and physical performance in preserving cognitive function-a decline that is not inevitable with age. Routine screening and early detection of muscle health and cognitive function are essential, and should be followed by intervention strategies targeting muscle health to mitigate cognitive decline in aging populations.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".