Relationship between sarcopenia and sleep status in female patients with mild to moderate Alzheimer's disease
Bibliographic record
Abstract
BACKGROUND: Sleep disorders and sarcopenia could contribute to the development of Alzheimer's disease (AD), which are risk factors that rapidly deteriorate cognitive functions. However, few studies have evaluated the relationship between sarcopenia and sleep disorders in female AD patients, who have a higher prevalence than male patients. This study aimed to investigate the relationship between sarcopenia and sleep status in female patients with mild to moderate AD. METHODS: This cross-sectional study recruited 112 female outpatients aged between 60 and 85 years. Demographic characteristics, appendicular skeletal muscle mass index (ASMI), grip strength, and gait speed were assessed. Sarcopenia was diagnosed according to criteria of the Asian Working Group for Sarcopenia. Pittsburgh Sleep Quality Index (PSQI) assessed sleep variables. Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) assessed cognitive function. Binary logistic regression models explored the relationship between sleep variables and cognitive function and sarcopenia, adjusting for potential cofounders. RESULTS: The outpatients were divided into 36 AD patients with sarcopenia (ADSa) and 76 AD patients without sarcopenia (ADNSa), with a prevalence of 32.1%. ADSa had lower ASMI, weaker grip strength, slower gait speed, a higher incidence of poor sleep quality and poorer cognitive function. Multivariate binary logistic regression analysis showed that high total scores of PSQI (odds ratio (OR) = 1.13), poor sleep quality (OR = 2.73), poor subjective sleep quality (OR = 1.83), low MMSE (OR = 0.77) and MoCA (OR = 0.76) scores were associated with high odds of sarcopenia. Compared to sleep time ≤ 15 min, >60 min (OR = 5.01) were associated with sarcopenia. Sleep duration <6 h (OR = 3.99), 8-9 h (OR = 4.48) and ≥9 h (OR = 6.33) were associated with sarcopenia compared to 7-8 h. CONCLUSIONS: More sleep symptoms and cognitive impairment exist in female patients with sarcopenia. The higher total scores of PSQI, poorer subjective sleep quality, longer sleep latency, excessive and insufficient sleep duration and poorer cognitive function are associated with higher odds of sarcopenia in female patients with mild to moderate AD.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".