Abstract
Background
Sarcopenia, characterized by age-related declines in muscle mass, strength, and function, shares common risk pathways with depression including chronic inflammation and physical inactivity. Sleep disorders can affect muscle health and emotional state, potentially exacerbating both conditions, but the relationship among the three has not been fully explored.
Methods
We conducted a longitudinal analysis of 4,067 participants from the China Health and Retirement Longitudinal Study (CHARLS; 2011–2015). Sarcopenia and its components were defined using the Asian Working Group for Sarcopenia (AWGS) 2019 criteria, and depressive symptoms were assessed with the 10-item Center for Epidemiological Studies Depression Scale (CESD-10). Multivariable logistic regression estimated odds ratios (ORs). Stratified analyses were performed by nighttime sleep, total sleep, and daytime napping status, and mediation analyses evaluated sleep duration measured in 2013….
Results
Baseline depressive were associated with incident sarcopenia during follow-up (OR = 1.32, 95% CI 1.08–1.61), low muscle mass (OR = 1.39, 95% CI 1.12–1.72) and low muscle strength (OR = 1.68, 95% CI 1.41–2.00), whereas no statistically significant association was observed for low physical performance (OR = 1.00, 95% CI 0.84–1.20). Stratified analyses suggested that these associations were more evident among individuals reporting shorter sleep duration (particularly < 6 h) and among non-nappers. Mediation analyses indicated a partial indirect association via sleep duration measured in 2013, accounting for approximately 11.8% of the total association.
Conclusion
Depressive symptoms were associated with higher odds of incident sarcopenia, primarily through low muscle strength and low muscle mass. Sleep patterns may contribute to heterogeneity in these associations and account for a modest proportion through an indirect pathway, supporting integrated mental health screening and sleep assessment in middle-aged and older adults.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-026-26852-0.
Keywords: sarcopenia, depression, sleep time, mediation analysis
Introduction
Sarcopenia is an age-related syndrome characterized by a reduction in skeletal muscle mass, accompanied by a decline in muscle strength or physical function [1] The prevalence of sarcopenia varies worldwide depending on diagnostic criteria, age, and the populations studied [2]. A systematic review and meta-analysis reported that the overall prevalence of sarcopenia is approximately 10% among individuals aged 60 years and older [3]. In China, meta-analyses based on the Asian Working Group for Sarcopenia (AWGS) criteria have reported a pooled prevalence of around 14% among older adults, with higher estimates (approximately 17%) among community-dwelling adults aged ≥ 65 years [4, 5].Sarcopenia not only compromises muscle mass and function but is also associated with a wide range of health problems and adverse outcomes, including falls, osteoporosis, cardiovascular disease, diabetes, chronic kidney disease, cognitive impairment, multimorbidity, and increased mortality [6, 7], Consequently, sarcopenia imposes a substantial burden on healthcare systems and society, underscoring the importance of identifying modifiable risk factors and implementing early interventions.
Depression is a common mental health issue characterized primarily by low mood and loss of interest [8], accompanied by other symptoms such as sleep disturbances, changes in appetite, fatigue, and low self-esteem. It is a leading cause of mental health-related disability worldwide [9]. Depression has been shown to be associated with various factors that may increase the risk of sarcopenia. Depression is closely linked to reduced physical activity [10], poor nutrition [11], and increased sedentary behavior [12], all of which may accelerate the process of muscle loss. Studies have indicated that individuals with depression are more likely to develop sarcopenia [13].
Sleep is crucial for maintaining muscle health. Both insufficient sleep duration and poor sleep quality can lead to a decline in muscle mass [14]. Sleep duration is also significantly associated with depression [15]. However, previous studies have primarily focused on the relationship between sarcopenia and depression [16, 17] and their interactions [18], with most being cross-sectional, critical knowledge gaps remain to be addressed: First, the relationships between specific sleep parameters (e.g., duration, efficiency, and sleep architecture) and the risk of sarcopenia need to be further clarified. Second, it remains unclear whether sleep disturbances mediate or modify the association between depression and sarcopenia. Additionally, the temporal dynamics and long-term interplay of these relationships need to be unraveled through longitudinal studies to elucidate their evolving patterns over time. Addressing these gaps will provide critical insights for developing multidimensional intervention strategies targeting sleep, mental health, and sarcopenia. Therefore, studying the relationship between sleep duration and sarcopenia is necessary for the prevention of sarcopenia and the development of interventions.
In this study, we investigated the relationship between depression and sarcopenia and assessed whether sleep duration and nap behavior would affect this relationship. We further conducted mediation analyses to evaluate the potential mediating role of sleep in the association between depressive symptoms and sarcopenia and its components.
Methods
This cohort study was approved by the institutional review boards of the central institutions in each field. All participants were informed and provided written informed consent. This study strictly followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for reporting cohort studies.
Study design and participants
CHARLS project aims to collect high-quality micro data representing households and individuals aged 45 and above in China to analyze issues related to population aging in China and promote interdisciplinary research on aging issues. The CHARLS national baseline survey was conducted in 2011 using a multi-stage probability proportional to size sampling method. The sample covers 450 villages, 150 counties, and 28 provinces, involving more than 10,000 households and over 17,000 individuals. CHARLS is an ongoing survey with follow-ups conducted every 2 to 3 years. Participants are interviewed face-to-face at home using computer-assisted personal interviewing technology. The survey content includes basic demographic information of the respondents and their families, transfer payments among family members, the health status of the respondents, medical insurance, employment, income, expenditure, and asset conditions, etc. In addition, CHARLS also includes 13 physical measurements and blood sample collection. To date, CHARLS has released five waves of national data: the baseline survey (Wave 1, 2011–2012) and follow-up surveys conducted in 2013 (Wave 2), 2015 (Wave 3), 2018 (Wave 4), and 2020 (Wave 5). Detailed information about CHARLS has been published in previous literature [19]. The CHARLS dataset can be downloaded from the CHARLS homepage at http://charls.pku.edu.cn/en. The CHARLS survey project was approved by the Peking University Biomedical Ethics Committee, and all participants were required to sign an informed consent form.
The study process is shown in Fig. 1. Participants were derived from the China Health and Retirement Longitudinal Study (CHARLS), using data from the baseline survey in 2011 (Wave 1), the first follow-up in 2013 (Wave 2), and the second follow-up in 2015 (Wave 3). A total of 17,705 respondents were initially included at baseline in 2011. The exclusion criteria were as follows: (1) missing sarcopenia data in 2011 (n = 3013) and diagnosed sarcopenia in 2011 (n = 2319); (2) missing depression data (n = 127); (3) missing sleep data in 2011 (n = 992); (4) missing age data (n = 61); (5) missing drinking data (n = 87); (6) missing BMI and waist circumference data (n = 1,859); (7) missing information on diseases (e.g., heart disease, cancer, diabetes); (8) missing blood biochemical indicators (e.g., triglycerides, total cholesterol, and C-reactive protein) (n = 2,502); (9) missing sleep data in 2013 (n = 199); (10) diagnosed sarcopenia in 2013 (n = 802); and (11) missing sarcopenia data at the 2015 follow-up (n = 1,500). After these exclusions, 4,067 participants remained for the final analysis.
Fig. 1.
The flow chart of participants selection process
Diagnosis of sarcopenia
Sarcopenia was assessed according to the criteria recommended by AWGS 2019 [1], which includes muscle strength, skeletal muscle mass, and physical performance. Grip strength (measured in kilograms) was measured using both the dominant and non-dominant hands, with participants gripping the Yuejian™ WL-1000 dynamometer as hard as possible. Each participant held the dynamometer at a right angle (90°) with both hands, and the test was performed in duplicate. The average value of the highest intensity data available was used. If a participant was unable to measure one hand for some reason, the maximum value of the other hand was recorded. According to AWGS 2019, the cut-off value for low grip strength is defined as less than 28 kg for men and less than 18 kg for women. Appendicular skeletal muscle mass (ASM) was estimated using a validated anthropometric equation for Chinese residents [20].
ASMM = 0.193* weight (kg) + 0.107* height (cm)-4.157*gender-0.037* age (years)-2.631.
Body weight was measured using the Omron™ HN-286 weighing scale, and height was measured using the Seca™ 213 stadiometer. For males, the gender was set as 1, and for females, it was set as 0. Some studies have shown that the ASM calculated using this formula is in good agreement with dual-energy X-ray absorptiometry (DXA) [20, 21]. Similar to previous studies [22], the cut-off value for low muscle mass was based on the gender-specific lowest 20% of the height-adjusted muscle mass (ASM/height²) in the study population, with females < 4.89 kg/m² and males < 6.79 kg/m². Regarding physical performance, we used the 5-time chair stand test, classifying low physical performance as a time > 12 s for the 5-time chair stand test.
Measurement of sleep time
We assessed total sleep duration by asking, “How many hours of sleep did you actually get at night in the past month? (Average hours per night).” Total sleep duration included the sum of nighttime sleep and nap time. Based on the participants’ responses, sleep duration was categorized into three groups: short sleep duration (≤ 6 h), medium sleep duration (6–8 h), and long sleep duration (> 8 h) [22, 23]. Nap status was divided into two groups: “with a nap” and “without a nap.”
Measurement of depression
Depressive symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10). A cut-off score of ≥ 10 was used to identify respondents with significant depressive symptoms [24].
Potential covariates
We considered sociodemographic characteristics and health-related factors in our study. Sociodemographic characteristics included age, gender, education level (illiterate, primary school, middle school, college and above), Marital status(Married, never married) and residence(rural, urban). Health-related factors included body mass index (BMI), waist circumference (Waist), systolic blood pressure (SBP), diastolic blood pressure (DBP), smoking status (yes/no), alcohol consumption (yes/no), blood indicators (glucose, creatinine, triglycerides, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, C-reactive protein), common diseases (hypertension, hyperlipidemia, hyperglycemia/diabetes, cancer, stroke), physical activity.
Statistical analysis
Based on the normality determined by the Kolmogorov–Smirnov test, continuous variables are reported as mean ± standard deviation (SD) or median with interquartile range. Categorical variables are presented as numbers (n) and corresponding percentages (%). Data analysis included one-way analysis of variance or Kruskal-Wallis test for continuous variables and chi-square test for categorical variables. To assess the relationship between sleep duration and depression in the sarcopenia population, multivariate logistic regression analysis was performed. Three models were constructed for adjustment: Model 1 was unadjusted; Model 2 adjusted for age, sex, educational level, residential area, and marital status only; and Model 3 further adjusted for smoking, drinking, systolic blood pressure (SBP), diastolic blood pressure (DBP), glucose, C-reactive protein (CRP), comorbidities, fracture history, and medication use in addition to the covariates included in Model 2. Odds ratios (OR) and 95% confidence intervals (CI) were obtained. Stratified analyses were conducted separately for nighttime sleep duration, total sleep duration, and nap duration to explore the moderating effects of different sleep patterns on the relationship between depression and sarcopenia. Stratified analyses were also performed based on age, gender, BMI, alcohol consumption, hypertension, hyperlipidemia, hyperglycemia/diabetes, and physical activity to explore the relationship between total sleep duration and depression across subgroups. All statistical analyses were conducted using SPSS 27.0 statistical software, with the significance level set at p < 0.05.
Result
Basic Characteristics of Participants
There were 4,067 participants in total, including 2,623 individuals without depression and 1,444 with depression (Table 1). The average age in the depression group was slightly higher than that in the non-depression group (58.27 vs. 57.21 years). Compared with those without depressive symptoms, participants with depressive symptoms were more likely to be female, have lower educational attainment, and report short sleep (< 6 h). They also showed lower handgrip strength and longer chair-stand time, and a higher prevalence of several comorbidities and disability.
Table 1.
Baseline characteristics
| Variables | Total (N = 4067) |
No depression (n = 2623) |
Depression (n = 1444) |
|---|---|---|---|
| Age | 57.59 ± 8.83 | 57.21 ± 8.78 | 58.27 ± 8.89 |
| Male | 2030 (49.9) | 874(60.5) | 570(39.5) |
| Education level | |||
| Illiteracy | 1766 (43.4) | 997(38.0) | 769(53.3) |
| Primary school | 942 (23.2) | 604(23.0) | 338(23.4) |
| Middle school | 924 (22.7) | 673(25.7) | 251(17.4) |
| ≥college | 435 (10.7) | 349(13.3) | 86(6.0) |
| Marital status | |||
| Married | 3715(91.3) | 2450(93.4) | 1265(87.6) |
| Never married | 352(8.7) | 173(6.6) | 179(12.4) |
| Residence | |||
| rural | 1355(33.3) | 937(35.7) | 418(29.0) |
| urban | 2712(66.7) | 1686(64.3) | 1026(71.0) |
| BMI (kg/m2) | 25.01 ± 3.78 | 25.47 ± 3.75 | 24.18 ± 3.92 |
| WC (cm) | 86.27 ± 11.75 | 86.30 ± 12.05 | 86.22 ± 11.20 |
| SBP (mm/Hg) | 127.97 ± 20.30 | 127.95 ± 19.79 | 128.01 ± 21.21 |
| DBP (mm/Hg) | 75.17 ± 12.10 | 75.25 ± 11.86 | 75.03 ± 12.54 |
| Smoking | 1266(31.1) | 890(34.0) | 376(26.0) |
| Drinking | 1402(34.5) | 998(38.1) | 404((28.0) |
| Glucose (mg/dl) | 109.55 ± 34.06 | 109.23 ± 33.85 | 108.41 ± 34.45 |
| Creatinine (mg/dl) | 0.76 ± 0.17 | 0.74 ± 0.16 | 0.76 ± 0.18 |
| TG (mg/dl) | 134.08 ± 96.05 | 132.64 ± 91.09 | 136.70 ± 104.44 |
| TC (mg/dl) | 192.90 ± 37.90 | 192.18 ± 37.72 | 194.21 ± 38.19 |
| LDL-C (mg/dl) | 50.39 ± 14.85 | 50.04 ± 14.72 | 51.04 ± 34.98 |
| HDL-C (mg/dl) | 116.48 ± 34.67 | 116.34 ± 34.51 | 116.73 ± 35.63 |
| C-Reactive Protein (mg/dl) | 2.58 ± 7.43 | 2.60 ± 7.62 | 2.55 ± 5.07 |
| Hypertension | 907(22.3) | 524(20.0) | 383(26.5) |
| Dyslipidemia | 390(9.6) | 366(9.0) | 435(10.7) |
| Diabetes or Hyperglycemia | 237(5.8) | 134(5.1) | 103(7.1) |
| Cancer or Malignant Tumor | 36(0.9) | 22(0.8) | 14(1.0) |
| Stroke | 80(2.0) | 28(1.1) | 52(3.6) |
| Mental illness | 38(0.9) | 17(0.7) | 21(1.5) |
| Liver disease | 119(2.9) | 55(2.1) | 64(4.4) |
| Kidney disease | 227(5.6) | 112(4.3) | 115(8.0) |
| Fracture | 54(1.3) | 29(1.1) | 25(1.7) |
| Disability | 621(15.3) | 312(11.9) | 309(21.4) |
| Antihypertensive medication | 676(16.6) | 435(16.6) | 241(16.7) |
| Antidiabetic medication | 134 (3.3) | 88 (3.4) | 46 (3.2) |
| Anticancer medication | 27 (0.7) | 18 (0.7) | 9 (0.6) |
| Cardiovascular medication | 51 (1.3) | 33 (1.3) | 18 (1.3) |
| Antidepressant medication | 33 (0.8) | 22 (0.8) | 11 (0.8) |
| Physical Activity | |||
| Low | 624(15.3) | 397(15.1) | 227(15.7) |
| Moderate | 697 (17.1) | 483(18.4) | 214(14.8) |
| High | 2746(67.5) | 1743(66.6) | 1003(69.5) |
| Sleep Time, h | |||
| < 6 | 1130(27.8) | 519(19.8) | 611(42.3) |
| 6–8 | 2628(64.6) | 1887(71.9) | 741(51.3) |
| > 8 | 309(7.6) | 217(8.3) | 92(6.4) |
| HGS | 31.09 ± 9.61 | 31.09 ± 9.61 | 26.32 ± 9.26 |
| ASMM | 7.15 ± 1.25 | 7.15 ± 1.25 | 6.89 ± 3.62 |
| 5-CST | 9.23 ± 3.42 | 9.23 ± 3.42 | 10.36 ± 4.53 |
Continuous variables are presented as mean (standard deviation, SD) or median (interquartile range, IQR), as appropriate. Categorical variables are presented as number (percentage, %). BMI body mass index, WC waist circumference, SBP systolic blood pressure, DBP diastolic blood pressure, TC total cholesterol, TG triglycerides, HDL-C high-density lipoprotein cholesterol, LDL-C high-density lipoprotein cholesterol, HGS hand grip strength, ASMM appendicular skeletal muscle mass, 5-CST five-time chair stand test
Relationship Between Depression and Sarcopenia
The association between depression and sarcopenia and its components is presented in Table 2. In the unadjusted model, depression was significantly associated with the risk of sarcopenia (OR = 1.75, 95% CI 1.46–2.09; p < 0.001). After adjusting for confounding factors, the association remained significant (OR = 1.32, 95% CI 1.08–1.61; p = 0.007). Among the components of sarcopenia, Depression was associated only with muscle strength and muscle mass, but not with physical performance.
Table 2.
Association between depression and the incidence of sarcopenia
| No depression | Depression | P value | |
|---|---|---|---|
| Sarcopenia | |||
| Model 1 | 1 (reference) | 1.75 (1.46–2.09) | < 0.001 |
| Model 2 | 1.37 (1.13–1.66) | < 0.001 | |
| Model 3 | 1.32 (1.08–1.61) | 0.007 | |
| Low muscle mass | |||
| Model 1 | 1 (reference) | 1.72 (1.42–2.08) | < 0.001 |
| Model 2 | 1.29 (1.05–1.59) | 0.016 | |
| Model 3 | 1.39 (1.12–1.72) | 0.003 | |
| Low muscle strength | |||
| Model 1 | 1 (reference) | 1.99 (1.70–2.33) | < 0.001 |
| Model 2 | 1.79 (1.51–2.12) | < 0.001 | |
| Model 3 | 1.68 (1.41 -2.00) | < 0.001 | |
| Low physical performance | |||
| Model 1 | 1 (reference) | 1.36 (1.20–1.55) | < 0.001 |
| Model 2 | 1.04 (0.87–1.23) | 0.681 | |
| Model 3 | 1.00 (0.84–1.20) | 0.960 | |
Model 1 was conducted without any adjustment
Model 2 was adjusted for age, sex, education level, residence, marital statusl
Model 3 was additionally adjusted for smoking, drinking, physical activity, C-Reative Protein, comorbidities(hypertension, dyslipidemia, diabetes, stroke, and cancer), fracture history, medication use
Relationship Between Depression and Sarcopenia in Different Sleep
Stratified associations between depression and sarcopenia by nighttime sleep duration are presented in Table 3. In stratified analyses by sleep duration, depressive symptoms were associated with incident sarcopenia among participants sleeping < 6 h (OR 1.39, 95% CI 1.01–1.92) and 6–8 h (OR 1.32, 95% CI 1.08–1.61), but not among those sleeping > 8 h. Depressive symptoms were consistently associated with low muscle strength in the < 6 h and 6–8 h groups, whereas no association was observed with low physical performance across sleep strata.
Table 3.
Association between depression and the incidence of sarcopenia in different Night sleep time
| Total Sleep time <6 h | Total Sleep time 6–8 h | Total Sleep time >8 h | |||||||
|---|---|---|---|---|---|---|---|---|---|
| No depression | Depression | P | No depression | Depression | P | No depression | Depression | P | |
| Sarcopenia | |||||||||
| Model 1 | 1 (reference) | 1.64 (1.20–2.23) | 0.002 | 1 (reference) | 1.59 (1.24–2.03) | < 0.001 | 1 (reference) | 1.47 (0.76–2.83) | 0.250 |
| Model 2 | 1.37 (0.98–1.90) | 0.064 | 1.37 (1.13–1.66) | 0.001 | 1.14 (0.54–2.40) | 0.738 | |||
| Model 3 | 1.39 (1.01–1.92) | 0.048 | 1.32 (1.08–1.61) | 0.007 | 0.97 (0.43–2.16) | 0.935 | |||
| Low muscle mass | |||||||||
| Model 1 | 1 (reference) | 1.45 (1.04–2.01) | 0.026 | 1(reference) | 1.67 (1.28–2.18) | < 0.001 | 1 (reference) | 1.32 (0.64–2.73) | 0.446 |
| Model 2 | 1.16 (0.82–1.65) | 0.405 | 1.27 (0.95–1.69) | 0.104 | 1.12 (0.50–2.54) | 0.783 | |||
| Model 3 | 1.24 (0.86–1.78) | 0.252 | 1.39 (1.12–1.72) | 0.003 | 1.11 (0.47–2.63) | 0.807 | |||
| Low muscle strength | |||||||||
| Model 1 | 1 (reference) | 1.60 (1.22–2.09) | < 0.001 | 1 (reference) | 2.04 (1.65–2.52) | < 0.001 | 1 (reference) | 1.27 (0.68–2.35) | 0.453 |
| Model 2 | 1.55 (1.15–2.07) | 0.004 | 1.79 (1.51–2.12) | < 0.001 | 1.20 (0.61–2.37) | 0.604 | |||
| Model 3 | 1.45 (1.07–1.96) | 0.017 | 1.68 (1.41 -2.00) | < 0.001 | 1.15 (0.57–2.32) | 0.703 | |||
| Low physical performance | |||||||||
| Model 1 | 1 (reference) | 1.20 (0.95–1.53) | 0.131 | 1 (reference) | 1.34 (1.13–1.59) | < 0.001 | 1 (reference) | 0.92 (0.56–1.50) | 0.725 |
| Model 2 | 1.07 (0.78–1.46) | 0.693 | 1.01 (0.81–1.26) | 0.950 | 0.79 (0.40–1.54) | 0.489 | |||
| Model 3 | 1.05 (0.76–1.46) | 0.756 | 1.00 (0.84–1.20) | 0.960 | 0.74 (0.41–1.33) | 0.321 | |||
Model 1 was conducted without any adjustment
Model 2 was adjusted for age, sex, education level, residence, marital statusl
Model 3 was additionally adjusted for smoking, drinking, physical activity, C-Reative Protein, comorbidities(hypertension, dyslipidemia, diabetes, stroke, and cancer), fracture history, medication use
The association between depression and sarcopenia stratified by total sleep duration (nighttime sleep plus naps) is summarized in Table 4. Depressive were associated with a higher risk of incident sarcopenia among participants with total sleep time < 6 h after full adjustment (Model 3: OR 1.50, 95% CI 1.09–2.08). Similar patterns were observed for low muscle mass (OR 1.74, 95% CI 1.22–2.49) and low muscle strength (OR 1.52, 95% CI 1.13–2.04) in the < 6 h group. In contrast, among participants with total sleep time 6–8 h, depressive symptoms were not significantly associated with incident sarcopenia (OR 1.23, 95% CI 0.93–1.62) or low muscle mass (OR 1.23, 95% CI 0.91–1.66), whereas the association with low muscle strength remained evident (OR 1.80, 95% CI 1.42–2.29). No significant association between depressive symptoms and low physical performance was observed across total sleep strata.
Table 4.
Association between depression and the incidence of sarcopenia in different total sleep time
| Sleep time <6 h | Sleep time 6–8 h | Sleep time >8 h | |||||||
|---|---|---|---|---|---|---|---|---|---|
| No depression | Depression | P | No depression | Depression | P | No depression | Depression | P | |
| Sarcopenia | |||||||||
| Model 1 | 1 (reference) | 1.94 (1.45–2.61) | < 0.001 | 1 (reference) | 1.59 (1.24–2.05) | < 0.001 | 1 (reference) | 1.04 (0.53–2.03) | 0.911 |
| Model 2 | 1.56 (1.14–2.14) | 0.005 | 1.22 (0.94–1.60) | 0.142 | 1.02 (0.49–2.10) | 0.963 | |||
| Model 3 | 1.50 (1.09–2.08) | 0.014 | 1.23 (0.93–1.62) | 0.149 | 1.25 (0.53–2.96) | 0.612 | |||
| Low muscle mass | |||||||||
| Model 1 | 1 (reference) | 2.07 (1.51–2.85) | < 0.001 | 1 (reference) | 1.51 (1.15–1.98) | 0.003 | 1 (reference) | 0.88 (0.42–1.87) | 0.745 |
| Model 2 | 1.62 (1.15–2.28) | 0.006 | 1.10 (0.82–1.47) | 0.531 | 0.94 (0.41–2.14) | 0.885 | |||
| Model 3 | 1.74 (1.22–2.49) | 0.002 | 1.23 (0.91–1.66) | 0.179 | 1.31 (0.50–3.42) | 0.582 | |||
| Low muscle strength | |||||||||
| Model 1 | 1 (reference) | 1.94 (1.49–2.53) | < 0.001 | 1 (reference) | 2.09 (1.68–2.59) | < 0.001 | 1 (reference) | 1.14 (0.65 -2.00) | 0.647 |
| Model 2 | 1.64 (1.24–2.17) | < 0.001 | 1.92 (1.52–2.42) | < 0.001 | 1.28 (0.67–2.42) | 0.457 | |||
| Model 3 | 1.52 (1.13–2.04) | 0.005 | 1.80 (1.42–2.29) | < 0.001 | 1.10 (0.53–2.28) | 0.804 | |||
| Low physical performance | |||||||||
| Model 1 | 1 (reference) | 1.53 (1.23–1.91) | < 0.001 | 1 (reference) | 1.32 (1.11–1.57) | 0.001 | 1 (reference) | 0.87 (0.52–1.46) | 0.606 |
| Model 2 | 1.31 (0.98–1.74) | 0.065 | 0.94 (0.74–1.18) | 0.565 | 0.93 (0.43–1.99) | 0.849 | |||
| Model 3 | 1.23 (0.92–1.66) | 0.165 | 0.92 (0.73–1.16) | 0.484 | 0.99 (0.41–2.37) | 0.984 | |||
Model 1 was conducted without any adjustment
Model 2 was adjusted for age, sex, education level, residence, marital statusl
Model 3 was additionally adjusted for smoking, drinking, physical activity, C-Reative Protein, comorbidities(hypertension, dyslipidemia, diabetes, stroke, and cancer), fracture history, medication use
The association between depression and sarcopenia stratified by daytime napping status is presented in Table S1. After stratification by noon napping status, depressive symptoms were associated with a higher risk of incident sarcopenia among participants without noon naps (OR = 1.34, 95% CI 1.01–1.78), and were also associated with higher risks of low muscle mass (OR = 1.51) and low muscle strength (OR = 1.48). In contrast, among participants with noon naps, depressive symptoms were not significantly associated with incident sarcopenia or low muscle mass, whereas the association with low muscle strength remained significant (OR = 1.94). No significant association was observed between depressive symptoms and low physical performance in either group.We also performed sensitivity analyses by additionally adjusting for BMI in Model 3, and the results were generally consistent (Tables S2–S5).
Mediation effect of sleep
We further examined the potential mediating role of sleep duration (2013) in the association between baseline depressive symptoms (2011) and incident sarcopenia outcomes (2015). As shown in Table S6 and Fig. 2, sleep duration exhibited a statistically significant indirect effect on incident sarcopenia (β_indirect = 0.005, 95% CI 0.001–0.009), accounting for 11.8% of the total effect, while the direct effect remained significant (β_direct = 0.037, 95% CI 0.016–0.055). Similar partial mediation was observed for low muscle mass (Table S6 and Figure S1; β_indirect = 0.005, 95% CI 0.002–0.009; proportion mediated = 11.0%) and low muscle strength (Table S6 and Figure S2; β_indirect = 0.009, 95% CI 0.005–0.014; proportion mediated = 11.6%), with significant direct effects in both analyses. For low physical performance, the indirect effect via sleep duration was also significant (Table S6 and Figure S3; β_indirect = 0.008, 95% CI 0.003–0.014), accounting for 24.1% of the total effect, whereas the direct effect was not statistically significant (β_direct = 0.027, 95% CI − 0.002–0.056).
Fig. 2.
Mediation of sleep time in the association between depressive and sarcopenia
Subgroup analyses
Subgroup analyses were conducted according to age, sex, BMI, drinking status, hypertension, dyslipidemia, hyperglycemia, and physical activity (Fig. 3). Interaction tests indicated that BMI was the only statistically significant effect modifier (P for interaction = 0.034), whereas no statistically significant interactions were observed for the other subgroup variables (P for interaction > 0.05). Among participants with BMI < 28, depressive symptoms were more strongly associated with sarcopenia (OR = 1.49, 95% CI 1.33–2.14).
Fig. 3.
Stratified analysis for depression and sarcopenia
Discussion
In this longitudinal analysis, we observed that depressive symptoms were associated with incident sarcopenia during follow-up and its components, with associations primarily evident for low muscle strength and low muscle mass rather than low physical performance. This pattern of results suggests that the depression–sarcopenia association may be more clearly reflected in the core components of muscle strength and muscle mass. In contrast, performance-based measures are influenced by a broader range of cardiopulmonary, neurologic, and musculoskeletal factors and are therefore more susceptible to confounding and measurement noise. Stratified analyses by daytime napping indicated that the associations of depressive symptoms with sarcopenia and low muscle mass were more evident among non-nappers but not among nappers. Mediation analyses suggested that sleep duration measured in 2013 accounted for a small proportion of the association between baseline depressive symptoms and sarcopenia-related outcomes in 2015 (approximately 11%), indicating that sleep may represent one potential pathway linking depressive symptoms to poorer muscle health, although a substantial direct association remained. For low physical performance, the indirect association via sleep accounted for a larger proportion and the direct association was not statistically significant, suggesting that sleep may be more closely related to performance-based outcomes.
In recent years, a growing number of studies have focused on the association between depressive symptoms and sarcopenia, as well as the relationship between depression and sleep, whereas few studies have examined the interplay among all three. Previous research on sarcopenia has demonstrated the correlation between sarcopenia and depressive symptoms in systematic reviews and meta-analyses [17]. Moreover, older adults with sarcopenia are more likely to suffer from depression [17, 24]. Longitudinal cohort studies have shown that sarcopenia can increase the risk of developing depressive symptoms [9], which is consistent with the findings of this study. Some studies have also reported a positive linear association between the severity of depressive symptoms and sleep disturbances among postmenopausal women [25]. The association between depression and sarcopenia may be mediated through multiple mechanisms. First, depression can lead to reduced physical activity in individuals, thereby affecting muscle strength and function [26, 27]. Additionally, depression may promote the occurrence of sarcopenia by influencing hormone levels (such as cortisol and testosterone) [11]and inflammatory responses (such as elevated C-reactive protein levels) [11, 18]. In this study, we also found that depression was significantly associated with low muscle strength and low muscle mass, but not with physical function, which warrants further investigation.
However, most of these studies have not taken into account the potential impact of sleep duration. This study further explores the moderating effect of sleep duration on the relationship between depression and sarcopenia, and finds that unhealthy sleep patterns (such as too short) can significantly enhance this association, which is consistent with the findings of previous studies on the relationship between sleep duration and sarcopenia [28, 29].
Additionally, The napping findings provide a plausible explanation for differences between nighttime and total sleep stratification. Daytime napping decreased the association between depressive symptoms and sarcopenia, particularly for low muscle mass. However, the association between depressive symptoms and low muscle strength was still observed regardless of daytime napping status. The moderating effect of sleep duration on the relationship between depression and sarcopenia may be related to the regulation of endocrine and metabolism by sleep. This may be because unhealth sleep duration (such as nighttime sleep duration < 6 h) may lead to hormonal imbalances (such as growth hormone and melatonin) [30]and increased inflammatory responses [31], thereby exacerbating the adverse effects of depression on muscle health. Napping may play a protective role in this relationship by compensating for nighttime sleep insufficiency or improving daytime fatigue [32]. Future studies should further quantify the duration and frequency of daytime napping to distinguish the potentially different effects of moderate compensatory naps versus disease-related excessive sleepiness on muscle health,, and should incorporate more granular and objective sleep measures (e.g., actigraphy and sleep apnea–related phenotypes).
Notably, subgroup analyses in the present study suggested that BMI may modify the association between depressive symptoms and sarcopenia, with a more pronounced association observed among participants with BMI < 28. Similar evidence has been reported elsewhere: BMI was nonlinearly and positively associated with sleep-disordered breathing (SDB), and the association was more evident among individuals without hypertension, suggesting that the strength of BMI-related associations may vary by underlying health context [33]. In our study, several factors may account for the BMI-stratified differences. First, a lower BMI in middle-aged and older adults may reflect limited nutritional reserves and a smaller muscle buffer; in this setting, depressive symptoms are often related to reduced appetite, insufficient protein intake, and decreased physical activity, and thus may be more likely to co-occur with adverse muscle-related outcomes. Second, among individuals with higher BMI, sarcopenia may more commonly present as sarcopenic obesity, characterized by excess adiposity coexisting with reduced muscle mass; in such cases, BMI alone may not adequately capture heterogeneity in body composition, and muscle mass estimated using body size–related anthropometric equations may introduce misclassification, potentially attenuating or obscuring the observed association. Third, the markedly wider confidence intervals in the BMI ≥ 28 group suggest a limited sample size and/or fewer events in this subgroup, leading to unstable estimates and reduced statistical power, thereby lowering the reliability of inferences regarding effect heterogeneity.
Our findings suggest that depressive symptoms are associated with higher odds of incident sarcopenia-related outcomes, and that sleep characteristics may help identify subgroups at higher risk. In clinical practice and community settings, integrating brief sleep screening (e.g., sleep duration and napping patterns) with routine depression assessment may facilitate early identification of older adults who could benefit from muscle health evaluation (e.g., grip strength and functional tests). For individuals with depressive symptoms, particularly those reporting short sleep, in addition to routine sarcopenia prevention measures (e.g., resistance exercise and nutritional/protein intake guidance), it may be appropriate to incorporate sleep health assessment and management together with psychological support to enable more comprehensive risk management. From a public health perspective, integrating sleep and mental health screening into healthy aging programs may facilitate risk stratification and early identification, thereby potentially reducing the burden of sarcopenia-related adverse outcomes.
Despite providing important insights into the relationship between depression, sleep duration, and sarcopenia, this study has several limitations. First, although depressive symptoms and sleep measures were assessed prior to the sarcopenia outcome, this study remains a longitudinal observational analysis, and therefore causal inference cannot be established. Second, sleep duration and napping were self-reported (referring to the past month), which may be subject to recall bias and misclassification, and we did not capture other sleep dimensions such as sleep quality, insomnia symptoms, or sleep apnea. Third, depressive symptoms were assessed using the CESD-10 questionnaire, which is a screening instrument rather than a clinical diagnosis, and measurement error may be present. Fourth, despite multivariable adjustment, residual confounding may remain due to unmeasured or imperfectly measured factors (e.g., diet/protein intake, medication details, comorbidity severity, changes in physical activity, and socioeconomic factors). Fifth, muscle mass was estimated using an anthropometric equation rather than imaging-based methods (e.g., DXA), which may introduce outcome misclassification.Finally, the study sample is mainly from a specific region, which may introduce selection bias and limit the generalizability of the study results.
These findings have potential clinical and public health implications. Clinicians may consider integrating mental health screening with assessment of sleep patterns and muscle-related indicators, particularly among adults with depressive symptoms and abnormal sleep. At the population level, comprehensive health promotion programs that address mental health, sleep regularity, physical activity, and nutrition—while clarifying optimal napping patterns—may help reduce the burden of sarcopenia in high-risk groups.
Conclusion
Using longitudinal CHARLS data, this study provides additional evidence that depressive symptoms were associated with higher odds of incident sarcopenia and its key components (low muscle strength and low muscle mass), whereas the association with low physical performance was less evident. The findings further suggest that sleep characteristics may be related to the strength of these associations: the depression–sarcopenia associations were more pronounced among individuals reporting short nighttime sleep (< 6 h). Stratified analyses by daytime napping indicated that the associations were mainly observed among non-nappers and were attenuated among nappers, particularly for low muscle mass. In addition, mediation analyses suggested that subsequent sleep duration may partially account for the associations between depressive symptoms and sarcopenia-related outcomes, although other pathways are also likely to contribute.
These findings highlight the value of an integrated assessment of mental health, sleep, and muscle health in the management of middle-aged and older adults. Future studies should incorporate more refined sleep phenotypes (e.g., sleep quality and sleep disorders), objective measurements, and repeated assessments of depression and sleep trajectories to further validate these associations, clarify potential mechanisms, and evaluate the effectiveness of multidomain interventions on muscle health outcomes.
Non-standard Abbreviations and Acronyms
BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, high-density lipoprotein cholesterol; HGS, hand grip strength; ASMM, appendicular skeletal muscle mass; 5-CST, five-time chair stand test.
Supplementary Information
Acknowledgements
We thank the China Health and Retirement Longitudinal Study team for providing data and training in using the datasets. We thank all volunteers and staff involved in this research.
Authors’ contributions
Y.Z., and Z.K. contributed to the manuscript equally. Y.Z., and Z.K. conceive the research idea, design the analysis, and advised on statistical analysis methods. Y.Z. drafted the manuscript. S.C., X.Z., L.P. and L.L. contributed to the discussion and critical revision of the manuscript for important intellectual content. All authors reviewed and approved the final manuscript.
Funding
This study was supported by.
(1) the Shenzhen Science and Technology Program (JCYJ20230807115308018).
(2) Sanming Project Of Medicine in Shenzhen (szzysm202311020).
Data availability
All CHARLS data are obtained from the China Health and Retirement Longitudinal Study (http://charls.pku.edu.cn/en).
Declarations
Ethics approval and consent to participate
The study was approved by the institutional review boards at CHARLS, and informed consent was obtained from all participants.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yuxin Zeng and Zezhi Ke contributed equally to this work.
Contributor Information
Litao Pan, ltpan@email.szu.edu.cn.
Lizhen Liao, Email: liaolizhen@gdpu.edu.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All CHARLS data are obtained from the China Health and Retirement Longitudinal Study (http://charls.pku.edu.cn/en).