The Relationship Between Sleep Disorders and Combination of Diabetes and Sarcopenia in Adults Aged 45 Years or Older: 10-Year Nationwide Prospective Cohort Study
Bibliographic record
Abstract
Background: With changes in lifestyle, the issue of sleep disorders is becoming increasingly common. Diabetes and sarcopenia have been found to be independently associated with sleep disorders. However, fewer studies have explored the interaction between the combination of diabetes and sarcopenia at different stages and sleep disorders. Objective: This study aimed to explore the relationship between the combination of diabetes and sarcopenia and the incidence of sleep disorders in adults aged 45 years and older. Methods: Based on data from the CHARLS (China Health and Retirement Longitudinal Study), we selected participants with comprehensive diagnostic information on diabetes and sarcopenia from 2011 who had normal sleep duration at baseline and checked their follow-up information of sleep duration from 2013, 2015, 2018, and 2020. Diabetes was classified into diabetes (D), prediabetes (PD), and nondiabetes (ND), and sarcopenia was divided into sarcopenia (S), possible sarcopenia (PS), and nonsarcopenia (NS). The participants were divided into DS, DPS, DNS, PDS, PDPS, PDNS, NDS, NDPS, and NDNS groups. Kaplan-Meier survival curves, the log-rank test, Cox proportional hazards regression, and restricted cubic spline models were used for statistical analysis. Results: A total of 4936 participants were included in this study. The DS group had the highest incidence of sleep disorders: 49.32%, 28.57%, 36.36%, and 80.00% in 2013, 2015, 2018, and 2020 respectively. In the crude model, compared with the NDNS group, the risk of sleep disorders was increased in the DS group (hazard ratio [HR] 1.707, 95% CI 1.196-2.437), PDS (HR 1.599, 95% CI 1.235-2.071), NDS (HR 1.465, 95% CI 1.282-1.674), and DPS group (HR 1.318, 95% CI 1.097-1.583). The risk was increased but not statistically significant in the PDPS group (HR 1.160, 95% CI 0.987-1.365). After adjusting for covariates, the risk of sleep disorders remained statistically significant in the DS group (HR 1.515, 95% CI 1.059-2.167) and was significantly higher in the PDS (HR 1.423, 95% CI 1.096-1.847) and NDS (HR 1.279, 95% CI 1.113-1.468) groups than that in the NDNS group. The nonlinear associations between appendicular skeletal muscle mass, grip strength, 5-time chair test, fasting plasma glucose, and sleep disorders were observed and described. Conclusions: The combination of diabetes and sarcopenia significantly increases the risk of sleep disorders in adults aged 45 years and older. and the implementation of progression control of both diabetes and sarcopenia may be helpful to prevent sleep disorders in this population.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".