Highlights
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Sleep duration shows a U-shaped association with sarcopenia prevalence in the US population.
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Sleep duration of about 6.5 h can significantly reduce the risk of sarcopenia.
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Further studies are needed to explore the underlying mechanisms.
Keywords: American; Sarcopenia, Sleep time; Cross-sectional study
Abstract
Background
The purpose of this study was to examine the relationship between sleep duration and risk of sarcopenia in in general U.S. population.
Methods
Utilizing publicly available data from the National Health and Nutrition Examination Survey spanning from 2011 to 2014, we explored the association between sleep duration and prevalence of sarcopenia. To investigate their relationship, we conducted weighted multivariate logistic regression analysis, restricted cubic splines (RCS) curve, and subgroup analysis.
Results
The study included 8,200 individuals, among whom 99 (0.9 %) had sarcopenia. The RCS curve revealed a U-shaped association of sarcopenia with sleep duration (P for nonlinearity = 0.020), showing that the risk of sarcopenia decreases with increasing sleep duration, reaching the lowest risk around 6.67 h. After controlling for underlying cofounders, compared to individuals with sleep duration < 5 h, the odds ratios with 95 % confidence intervals of sarcopenia were 0.64 (0.27, 1.49), 0.50 (0.20, 1.26), 0.65 (0.27, 1.60), and 2.31 (0.73, 7.30) for < 5–6, 6.5–7.5, 8–9, and > 9 h group. The U-shaped association between sleep time and prevalence of sarcopenia also was observed in the subjects who aged < 40 or ≥ 40 years, were male or female, with or without hypertension, and diabetes mellitus.
Conclusions
In summary, both short and long sleep durations increased prevalence of sarcopenia. Further studies are needed to explore the underlying mechanisms.
1. Introduction
Sarcopenia, characterized by the progressive decline in muscle mass and strength accompanying aging, stands as a significant and burgeoning public health concern (Denison et al., 2015, Fielding et al., 2011). As demographic shifts usher in an aging population, the anticipated rise in sarcopenia prevalence portends an upsurge in correlated health complications and societal burdens, with estimates ranging from approximately 10 %-16 % in elderly and 18 %-66 % in patients with diseases such as diabetes (Denison et al., 2015, Piovezan et al., 2015, Woo, 2017, Yuan and Larsson, 2023). Though prevalence tends to increase with advancing age, it is also recognized in younger age groups, especially in individuals with specific health conditions or sedentary lifestyles. Its deleterious implications include heightened susceptibility to falls, fractures, diminished mobility, and potentially fatal outcomes (Woo, 2017, Yuan and Larsson, 2023). Consequently, there arises an imperative to discern modifiable risk factors underpinning sarcopenia to establish effective preventive measures.
Among the array of putative modifiable risk factors, sleep duration emerges as a focal point of interest. Precedent research indicates an association between sleep duration and sarcopenia, with emerging evidence suggesting that poor sleep patterns, including inadequate sleep duration or sleep disorders, might contribute to sarcopenia risk (Li et al., 2023, Pourmotabbed et al., 2020). Notably, insufficient sleep duration has correlated with elevated systemic inflammation levels, a recognized precursor to sarcopenia (Cappuccio et al., 2008). Furthermore, truncated sleep has exhibited an ability to heighten cortisol levels, precipitating muscular catabolism and consequent muscle loss. Conversely, excessive sleep duration may also bear relevance to sarcopenia, attributable to its adverse impact on physical activity patterns and overall health (Kim et al., 2018, Shibuki et al., 2023). Despite the prospective significance of sleep duration as a malleable risk factor in sarcopenia development, the body of research delineating the precise association between sleep duration and sarcopenia prevalence remains scant. Hence, the principal aim of this comprehensive cross-sectional study was to probe this relationship within a demographically representative sample sourced from the United States (U.S.) population.
2. Material and Methods
2.1. Study population
This study used data from the 2011–2014 National Health and Nutrition Examination Survey (NHANES), a representative sample of the U.S. population. NHANES collects data on a wide range of health and nutrition variables, including sleep time and sarcopenia prevalence (Xiao et al., 2023). The sample included 19,134 individuals aged 16 years and above. The flowchart (Fig. 1) outlines the participants included in the study. After excluding those with missing data on sarcopenia (n = 8,061) and sleep time (n = 2,873), the number of participants for analysis is reduced to 8,200 for our data analysis.
Fig. 1.
Study flow chart. Abbreviations: NHANES, National Health and Nutrition Examination Surveys.
2.2. Sleep time
Sleep duration was extracted from a self-reported questionnaire and the participants was asked using the question, “Number of hours usually sleep on weekdays or workdays?”, and the average sleep duration was calculated. A brochure with further information on the questionnaire data may be found at https://wwwn.cdc.gov/Nchs/Nhanes/2017–2018/SLQ_J.htm. In our study, sleep time was divided in 5 levels: < 5 h, 5 to 6 h, 6.5 to 7.5 h, 8 to 9 h, > 9 h and the sleep duration < 5 h served as the reference group (Yang et al., 2018).
2.3. Sarcopenia ascertainment
Sarcopenia was diagnosed using dual-energy X-ray absorptiometry (DXA) scan. The DXA scan provides a precise measurement of muscle mass and strength, making it an accurate method for diagnosing sarcopenia. Appendicular lean mass (ALM) was defined as the sum of the fat-free masses of all four extremities (arms and legs). For this study, Foundation for the National Institutes of Health criteria was used for ALM-defined sarcopenia (<19.75 kg in males, <15.02 kg in females) and ALM adjusted for body mass index (BMI) (<0.789 kg for males, <0.512 kg for females) (Batsis et al., 2017, Rippberger et al., 2018).
2.4. Covariates
The following socio-economic covariates were included in the study: age, sex, race/ethnicity, family poverty income ratio (PIR), education level and marital status. Lifestyle behavior including smoking status (no, former, now) and alcohol use (no, former, mild, moderate and heavy) were included. Non-communicable diseases such as hypertension, diabetes mellitus (DM), CHD, CHF, angina pectoris, heart attack and stroke were involved and were categorized dichotomously. Continuous covariates including body mass index (BMI), mean energy intake, blood urea nitrogen (BUN), fast glucose (FBG), calcium (Ca), high-density lipoprotein-cholesterol, waist circumference, total cholesterol, hemoglobin (Hb), triglyceride, serum uric acid (UA), serum creatinine (Scr), phosphorus, and estimated glomerular filtration rate (eGFR) were also included.
2.5. Statistical analysis
All analyses were conducted using R version 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria). P-value < 0.05 was considered statistically significant. In this study, all NHANES estimations were based on sample weights computed. The questionnaire was administered first, and the total population of the United States/the number of people who participated in the questionnaire = wtint2yr. We selected 2 years of survey samples for merging and then conducted data analysis survey. The survey data of every 2 years have corresponding weights. After reasonable selection of weights, the combined years can be according to the following formula: Weight = 1/4*(wtint2yr (2011–2012) + wtint2yr (2013–2014)). Continuous variables are expressed as means (standard deviations, SDs), and categorical variables are presented as numbers (%). To calculate differences between groups, we used weighted Student’s t-test (continuous variables) and weighted chi-square tests (categorical variables). The weighted regression models adjust the parameter estimates and standard errors based on the assigned weights. To evaluate the associations between sleep duration and sarcopenia and explore the potential nonlinearity of their associations, weighted restricted cubic spline (RCS) plot and multivariate logistic regression model were first conducted. Three models were constructed, as follows: Model 1 was adjusted for age and sex; model 2 was further adjusted for race/ethnicity, education level, marital status, family PIR, the complication of hypertension, DM, smoking, and drink status based on model 1; based on model 2, model 3 was then adjusted for waist circumference, HDL-C, Hb, Ca, phosphorus, TC, FBG, TG, BUN, albumin, UA, mean energy intake, Scr, and eGFR, as the final model. Finally, subgroup analyses were used to evaluate the relationship between sleep time and sarcopenia based on age, sex, hypertension, and DM. We utilized a 1:3 propensity score matching (PSM) technique to balance differences between individuals with non-sarcopenia and sarcopenia. Baseline characteristics were adjusted for potential confounding variables such as age, marital status, sex, family poverty income ratio (PIR), hypertension, education level, race, and DM. The PSM data were subsequently re-analyzed to validate the accuracy of the findings.
3. Results
3.1. Baseline characteristics
Table 1 presents the demographic characteristics of the study participants, including both non-sarcopenia and sarcopenia groups. The overall sample size is 8,200 participants, with 8,131 in the non-sarcopenia group and 99 in the sarcopenia group. The mean age of the overall sample is 37.49 ± 0.35 years, with the non-sarcopenia group having a mean age of 37.40 ± 0.36 years and the sarcopenia group having a significantly higher mean age of 44.07 ± 1.66 years (P < 0.001). The non-Hispanic White group represents the largest proportion of the sample (35.9 %), followed by non-Hispanic Black (23.7 %) and Mexican American (13.5 %). Compared with 4,053 male participants (49.4 %) in the non-sarcopenia group, only 66 male participants (0.8 %) in the sarcopenia group, which may suggest that sarcopenia is less common in males. Further information such as education level, family PIR, marital status, smoking status, alcohol use, hypertension, diabetes mellitus (DM) was presented in Table 1. Finally, we also compared the characteristics of the populations between the those with missing values and those without in Supplementary Table 1. There was statistical evidence showing those excluded from this study were older, more non-Hispanic Black or White, more educated, and more likely to have a partner.
Table 1.
Demographic characteristics of the study participants from NHANES 2011–2014 (n = 8,200).
| Variables | Overall (n = 8,200) | Non-sarcopenia (n = 8,131) | Sarcopenia (n = 99) | P-value |
|---|---|---|---|---|
| Age, years | 37.49 ± 0.35 | 37.40 ± 0.36 | 44.07 ± 1.66 | <0.001(#) |
| Sex (%) | 0.003(&) | |||
| Male | 4053 (49.4) | 3987 (49.2) | 66 (66.7) | |
| Female | 4147 (50.6) | 4114 (50.8) | 33 (33.3) | |
| Race (%) | 0.400(&) | |||
| Mexican American | 1103 (13.5) | 1083 (13.4) | 20 (20.2) | |
| Other Hispanic | 766 (9.3) | 759 (9.4) | 7 (7.1) | |
| Non-Hispanic Black | 1941 (23.7) | 1922 (23.7) | 19 (19.2) | |
| Non-Hispanic White | 2940 (35.9) | 2899 (35.8) | 41 (41.4) | |
| Other race | 1450 (17.7) | 1438 (17.8) | 12 (12.1) | |
| Family PIR | 2.81 ± 0.08 | 2.82 ± 0.08 | 2.72 ± 0.21 | 0.632(#) |
| Education level (%) | 0.831(&) | |||
| Less than high school | 2048 (25.0) | 2017 (24.9) | 31 (31.3) | |
| High school | 1761 (21.5) | 1737 (21.4) | 24 (24.2) | |
| More than high school | 4391 (53.5) | 4347 (53.7) | 44 (44.5) | |
| Marital status (%) | 0.189(&) | |||
| Having a partner | 4396 (53.6) | 4349 (53.7) | 47 (47.5) | |
| No partner | 1034 (12.6) | 1015 (12.5) | 19 (19.2) | |
| Unmarried | 2770 (33.8) | 2737 (33.8) | 33 (33.3) | |
| Smoker (%) | 0.642(&) | |||
| No smoking | 5113 (62.4) | 5051 (62.4) | 62 (62.6) | |
| Former | 1197 (14.6) | 1177 (14.5) | 20 (20.2) | |
| Now | 1890 (23.0) | 1873 (23.1) | 17 (17.2) | |
| Alcohol user (%) | < 0.001(&) | |||
| No drinking | 1410 (17.2) | 1390 (17.2) | 20 (20.2) | |
| Former | 885 (10.8) | 864 (10.7) | 21 (21.2) | |
| Mild | 2358 (28.8) | 2331 (28.8) | 27 (27.3) | |
| Moderate | 1434 (17.5) | 1428 (17.6) | 6 (6.1) | |
| Heavy | 2113 (25.8) | 2088 (25.8) | 25 (25.3) | |
| Hypertension (%) | < 0.001(&) | |||
| No | 6154 (75.0) | 6122 (75.6) | 32 (32.3) | |
| Yes | 2046 (25.0) | 1979 (24.4) | 67 (67.7) | |
| DM (%) | < 0.001(&) | |||
| No | 7433 (90.6) | 7366 (90.9) | 67 (67.7) | |
| Yes | 767 (9.4) | 735 (9.1) | 32 (32.3) | |
| CHD (%) | < 0.001(&) | |||
| No | 8116 (99.0) | 8025 (99.1) | 91 (91.9) | |
| Yes | 84 (1.0) | 76 (0.9) | 8 (9.5) | |
| CHF (%) | < 0.001(&) | |||
| No | 8113 (98.9) | 8021 (99.0) | 92 (92.9) | |
| Yes | 87 (1.1) | 80 (1.0) | 7 (7.1) | |
| Angina pectoris (%) | 0.009(&) | |||
| No | 8131 (99.2) | 8034 (99.2) | 97 (98.0) | |
| Yes | 69 (0.8) | 67 (0.8) | 2 (2.0) | |
| Heart attack (%) | < 0.001(&) | |||
| No | 8103 (98.8) | 8014 (98.9) | 89 (89.9) | |
| Yes | 97 (1.2) | 87 (1.1) | 10 (10.1) | |
| Stroke (%) | 0.171(&) | |||
| No | 8096 (98.7) | 8000 (98.8) | 96 (97.0) | |
| Yes | 104 (1.3) | 101 (1.2) | 3 (3.0) | |
| BMI, kg/m2 | 28.54 ± 0.17 | 28.32 ± 0.16 | 44.12 ± 1.01 | < 0.001(#) |
| Waist circumference, cm | 97.01 ± 0.41 | 96.55 ± 0.40 | 130.45 ± 1.86 | < 0.001(#) |
| Mean energy | 2180.39 ± 11.30 | 2180.26 ± 11.03 | 2189.53 ± 84.39 | |
| intake (kcal/day) | 0.910(#) | |||
| FBG, mg/dL | 101.38 ± 0.43 | 101.01 ± 0.42 | 128.18 ± 6.72 | < 0.001(#) |
| Hb, g/dL | 14.24 ± 0.04 | 14.23 ± 0.04 | 14.49 ± 0.27 | 0.330(#) |
| Albumin, g/L | 43.39 ± 0.08 | 43.41 ± 0.08 | 41.53 ± 0.45 | < 0.001(#) |
| Ca, mg/mL | 9.43 ± 0.01 | 9.43 ± 0.01 | 9.30 ± 0.06 | 0.022(#) |
| Phosphorus, mg/mL | 3.82 ± 0.01 | 3.82 ± 0.01 | 3.69 ± 0.08 | 0.132(#) |
| TC, mg/dL | 189.46 ± 0.81 | 189.44 ± 0.84 | 190.32 ± 6.63 | 0.900(#) |
| TG, mg/dL | 122.14 ± 1.94 | 121.56 ± 1.96 | 163.65 ± 8.43 | < 0.001(#) |
| HDL-C, mg/dL | 52.11 ± 0.31 | 52.25 ± 0.31 | 41.89 ± 1.07 | < 0.001(#) |
| BUN, mg/dL | 11.93 ± 0.07 | 11.91 ± 0.07 | 13.38 ± 0.71 | 0.045(#) |
| UA, mg/dL | 5.32 ± 0.03 | 5.31 ± 0.03 | 6.34 ± 0.22 | < 0.001(#) |
| Scr, mg/dL | 0.86 ± 0.01 | 0.86 ± 0.01 | 0.89 ± 0.03 | 0.293(#) |
| eGFR, ml/min/1.73 m2 | 103.31 ± 0.45 | 103.35 ± 0.46 | 99.99 ± 2.53 | 0.215(#) |
| Sleep times, hours | 6.88 ± 0.02 | 6.88 ± 0.02 | 6.89 ± 0.15 | 0.951(#) |
Abbreviations: DM, diabetes mellitus; BMI, body mass index; CHD, coronary heart disease; CHF, congestive heart failure; Hb, hemoglobin; HbA1c, glycosylated hemoglobin; FBG, fast glucose; HbA1c, glycosylated hemoglobin; BUN, blood urea nitrogen; UA, uric acid; Scr, serum creatinine; TC, total cholesterol; TG, triglycerides; HDL-cholesterol, high density lipoprotein-cholesterol; eGFR, estimated glomerular filtration rate. Data are presented as mean ± SD or n (%); The P-value of continuous variables P-value came from weighted Student’s t-test and presented as ‘#’; The P-value of categorical variable came from weighted chi-square tests and presented as ‘&’.
3.2. Association between sleep time and sarcopenia
In Table 2, after adjusting for age and sex factors (Model 1), significant associations of sleep time 6.5–7.5 h with the risk of sarcopenia were observed, with adjusted ORs 0.41 (95 %CI: 0.18–0.91). However, after further adjustment for other potential confounding factors (Model 2 and Model 3), the association between sleep duration and the prevalence of sarcopenia remained nonsignificant. The findings depicted in Fig. 2 reveal a U-shaped trend concerning sarcopenia prevalence in relation to varying sleep durations (P for nonlinearity = 0.020). Notably, when sleep duration surpasses 6.67 h, there is a substantial surge in the risk of developing sarcopenia, denoted by an adjusted OR of 1.39 (95 %CI: 1.03–1.86). Conversely, a diminishing trend in sleep time from 6.67 h is accompanied by an observable escalation in the risk of sarcopenia, yet this trend lacks statistical significance, revealing an OR of 0.89 (95 % CI: 0.61–1.27). After PSM reanalysis, the RCS curve again verified that sleep time was negatively and U-shaped trend related to sarcopenia (P for nonlinearity = 0.020; Supplementary Fig. 1).
Table 2.
Adjusted ORs for association of sleep time and prevalence of sarcopenia in individuals from NHANES 2011–2014.
| Sleep time | Model 1 | Model 2 | Model 3 |
|---|---|---|---|
| OR (95 %CI) | OR (95 %CI) | OR (95 %CI) | |
| < 5 h | Ref. | Ref. | Ref. |
| 5–6 h | 0.62 (0.29, 1.29) | 0.67 (0.31, 1.43) | 0.64 (0.27, 1.49) |
| 6.5–7.5 h | 0.41 (0.18, 0.91) | 0.48 (0.21, 1.12) | 0.50 (0.20, 1.26) |
| 8–9 h | 0.51 (0.24, 1.11) | 0.56 (0.25, 1.26) | 0.65 (0.27, 1.60) |
| >9h | 2.58 (0.99, 6.68) | 2.06 (0.76, 5.55) | 2.31 (0.73, 7.30) |
| P for trend | 0.918 | 0.900 | 0.498 |
Abbreviations: OR, odd ratio; CI, confidence interval; Model 1: age and sex. Model 2: model 1 variables plus race/ethnicity, education level, marital status, family poverty income ratio, the complication of hypertension, and diabetes mellitus, smoke status, and drink status. Model 3 was adjusted for model 2 variables plus body mass index, waist circumference, coronary heart disease, congestive heart failure, angina pectoris, heart attack, heart attack, mean energy intake, hemoglobin, fast glucose, albumin, calcium, phosphorus, high-density lipoprotein-cholesterol, total cholesterol, triglyceride, blood urea nitrogen, serum uric acid, serum creatinine, and estimated glomerular filtration rate.
Fig. 2.
RCS curve for the relationship between sleep time and prevalence of sarcopenia. Abbreviations: RCS, restricted cubic spline.
3.3. Subgroup analyses
Subgroup analyses in Table 3 and Fig. 3A, 3B, 3C, and 3D presents the associations between sleep time and sarcopenia in different groups. Notably, a discernible U-shaped dose–response relationship between sleep duration and sarcopenia prevalence is observed among individuals aged < 40 or ≥ 40 years, were male or female, with or without hypertension, and DM.
Table 3.
Subgroup analysis for association of sleep time and prevalence of sarcopenia in individuals from NHANES 2011–2014.
| Sleep time | <5h | 5–6 h | 6.5–7.5 h | 8–9 h | >9h | P for trend | P for interaction |
|---|---|---|---|---|---|---|---|
| OR (95 %CI) | OR (95 %CI) | OR (95 %CI) | OR (95 %CI) | OR (95 %CI) | |||
| Age | 0.085 | ||||||
| < 40 | Ref. | 0.46 (0.09, 2.37) | 0.52 (0.10, 2.70) | 0.52 (0.10, 2.62) | 2.92 (0.49, 1.74) | 0.214 | |
| ≥ 40 | Ref. | 0.80 (0.33, 1.92) | 0.44 (0.16, 1.22) | 0.58 (0.22, 1.53) | 1.65 (0.44, 6.20) | 0.489 | |
| Sex | 0.035 | ||||||
| Male | Ref. | 0.87 (0.29, 2.65) | 0.77 (0.24, 2.45) | 0.76 (0.24, 2.41) | 2.87 (0.74, 11.14) | 0.555 | |
| Female | Ref. | 0.56 (0.19, 1.68) | 0.19 (0.04, 0.84) | 0.38 (0.12, 1.29) | 1.78 (0.37, 8.65) | 0.463 | |
| Hypertension | 0.146 | ||||||
| No | Ref. | 0.36 (0.10, 1.37) | 0.21 (0.05, 0.91) | 0.29 (0.07, 1.13) | 1.76 (0.36, 8.68) | 0.977 | |
| Yes | Ref. | 0.87 (0.34, 2.22) | 0.67 (0.24, 1.87) | 0.77 (0.29, 2.09) | 2.00 (0.56, 7.18) | 0.851 | |
| DM | 0.008 | ||||||
| No | Ref. | 0.85 (0.28, 2.54) | 0.82 (0.26, 2.57) | 1.01 (0.33, 3.06) | 2.49 (0.62, 9.91) | 0.267 | |
| Yes | Ref. | 0.60 (0.19, 1.91) | 0.19 (0.04, 0.90) | 0.19 (0.04, 0.80) | 3.85 (0.73, 20.46) | 0.238 | |
Abbreviations: DM, diabetes mellitus; OR, odd ratio; CI, confidence interval; Analysis was adjusted for age, sex, race/ethnicity, education level, marital status, family poverty income ratio, the complication of hypertension, and DM, body mass index, waist circumference, coronary heart disease, congestive heart failure, angina pectoris, heart attack, heart attack, mean energy intake, hemoglobin, fast glucose, albumin, calcium, phosphorus, high-density lipoprotein-cholesterol, total cholesterol, triglyceride, blood urea nitrogen, serum uric acid, serum creatinine, and estimated glomerular filtration rate.
Fig. 3.
RCS curve for the relationship between sleep time with prevalence of sarcopenia (A) the association of sleep time with sarcopenia stratified by age; (B) the association of sleep time with sarcopenia stratified by sex; (C) the association of sleep time with sarcopenia stratified by hypertension; (D) the association of sleep time with sarcopenia stratified by DM. Abbreviations: RCS, restricted cubic spline; BMI, body mass index; DM, diabetes mellitus.
4. Discussion
This study suggests that both short and long sleep times are associated with an increased prevalence of sarcopenia in the general U.S. population. The U-shaped relationship was observed in subjects who were < 40 or ≥ 40 years, male or female, with or without hypertension, and DM. Sleep duration of about 6.5 h can significantly reduce the risk of sarcopenia. These results highlight the importance of promoting optimal sleep duration in efforts to mitigate the burden of sarcopenia in our general population. Future longitudinal studies are needed to confirm these findings and to investigate potential mechanisms underlying the observed associations.
Our findings align with previous studies, indicating a potential association between both insufficient and excessive sleep durations and an elevated risk of sarcopenia (Cappuccio et al., 2008, Li et al., 2023, Pourmotabbed et al., 2020). For instance, several meta-analyses confirmed an independent link between short and long sleep durations and heightened sarcopenia risk in older adults, revealing a U-shaped relationship between sleep duration and sarcopenia prevalence (Li et al., 2023, Pourmotabbed et al., 2020). Moreover, our study found that individuals under 40 years old with extended sleep periods might face heightened susceptibility to sarcopenia, which is aligned with recent work by Li et al (Li et al., 2023). This association could stem from prolonged unhealthy behaviors in young people e.g., alcohol use, smoking, sedentary behaviors or inadequate physical activity, potentially leading to muscle disuse and atrophy, consequently contributing to sarcopenia progression (Lucassen et al., 2017). Furthermore, our findings suggest a potential gender disparity in the relationship between sleep duration and sarcopenia risk, where compared with male, female with less sleep duration might be more susceptible to the risk of sarcopenia. Existing evidence points to gender-specific variations in muscle composition and physiology, particularly sex hormones and the prevalence of type I (slow-twitch) muscle fibers, which could respond differently to alterations in sleep patterns, impacting muscle health (Kamei et al., 2004, Kim et al., 2018, Lucassen, E.A., De Mutsert, R., Le Cessie, S., Appelman-Dijkstra, N.M., Rosendaal, F.R., Van Heemst, D., Den Heijer, M., Biermasz, N.R., Group, N.S., 2017). Nonetheless, it is imperative to note that the complex interplay among sleep duration, gender, age, and sarcopenia risk is influenced by an array of factors encompassing lifestyle, genetics, hormonal variances, and individual health conditions, wherein identifying individuals' sleep behavior stages facilitates tailored interventions (Chen et al., 2014, Messier et al., 2011). Moreover, encouraging better sleep practices and optimizing sleep quality and duration in specific groups might mitigate factors contributing to sarcopenia development or progression, potentially enhancing muscle health and reducing the prevalence of this condition. The correlation between sleep duration and the risk of sarcopenia may be explicable through a spectrum of biological, neurological, and metabolic pathways (Lang et al., 2010, Woo, 2017). Primarily, investigations indicate that sleep duration exerts influence over hormonal equilibrium, notably impacting the secretion of growth hormone, a key regulator in muscle growth, repair, and maintenance (Balbo et al., 2010). Perturbations in sleep patterns might compromise this hormonal balance, potentially leading to muscle atrophy associated with sarcopenia (Paddon-Jones and Rasmussen, 2009). Additionally, inadequate sleep or sleep deprivation has been associated with an inflammatory response, contributing to heightened systemic inflammation (Kwon et al., 2017). Elevated inflammatory markers can stimulate muscle degradation and impede muscle function, thereby potentially exacerbating sarcopenia (de Sá Souza et al., 2022). From a neurological standpoint, disruptions in sleep patterns affect neural functionality, impacting both motor control and coordination (Woo, 2017). Prolonged inadequate sleep might compromise neuromuscular coordination, gradually impairing muscle performance over time (Vitale et al., 2019). Moreover, the intricate influence of sleep duration on metabolic processes, encompassing glucose metabolism and insulin sensitivity, has been documented (Pourmotabbed et al., 2020). Suboptimal sleep patterns may disrupt these metabolic pathways, fostering muscle wasting and furthering the development of sarcopenia (Ida et al., 2019). Additionally, sleep disturbances have been observed to elevate stress hormone levels, particularly cortisol, potentially instigating muscle breakdown and inhibiting muscle protein synthesis, thereby contributing to the onset or progression of sarcopenia (Prokopidis and Dionyssiotis, 2021). Beyond these outlined pathways, alterations in sleep duration corresponding to individual physiological conditions such as aging, disease development, and physical inactivity might also impact the development of sarcopenia (Li et al., 2023). Additionally, Huang et al. reported the lowest sarcopenia risk among people aged 65 + when sleeping 9.3 h in Taiwan (Huang et al., 2022), inconsistent with our findings. Different populations could have contributed to the different results. Their study population is Taiwan population, this study population is the United States population. Furthermore, their study focused on people over 65 years old, while the study's population was < 60 years. Moreover, the dissimilar findings might result from the daytime nap. Young and old people have different nap habits. Finally, sarcopenia was regarded as a disease related to aging (Larsson et al., 2019). Further research is needed to determine how the circadian rhythm affects the risk of sarcopenia. In summary, these interconnected mechanisms often operate synergistically, potentially influencing the intricate relationship between sleep duration and the genesis or advancement of sarcopenia. Further comprehensive research is imperative to delineate these complex pathways and establish causal associations between sleep patterns and the initiation or exacerbation of sarcopenia.
Our study exhibits several strengths. First, the study uses a large sample size, allowing for a more representative and comprehensive analysis of the relationship between sleep time and sarcopenia prevalence. By including a large number of participants, the study increases the statistical power of the analysis (Mann, 2003). The study adjusts for several confounding variables such as age, gender, physical activity level, and nutrition status, strengthening the internal validity of the findings. By controlling for these factors, the researchers can better isolate the independent effect of sleep time on sarcopenia prevalence. However, our study has few limitations. Firstly, the participants were recruited from specific health centers and may not be representative of the general population. Therefore, the results may not be widely generalizable to other populations. Secondly, the study's design, rooted in cross-sectional analysis, precludes the establishment of causality, providing solely an association between sleep duration and sarcopenia at a singular point in time (Flanders et al., 1992). Longitudinal studies are needed to determine the directionality and temporal relationships between sleep time and sarcopenia. Thirdly, reliance on self-reported sleep duration introduces the possibility of recall bias and measurement error, casting uncertainty on the accuracy of reported sleep patterns (Spector, 2019). Finally, it is well known that physical activity and even sedentary time are important for sarcopenia risk and sleep. However, these variables were not included or controlled for in the study. Future research should consider these limitations and design more rigorous studies to further investigate the relationship between sleep time and sarcopenia.
5. Conclusion
In conclusion, the results of this study demonstrate that there is a significant U-shaped correlation between sleep time and sarcopenia prevalence. Additional research is required to establish causal relationships and to develop targeted interventions for sarcopenia prevention and management.
Funding statement
This work was supported by the School Foundation of Anhui Medical University (2021xkj065) and the Research Fund of Anhui Institute of translational medicine (2022zhyx-C90).
Author contributions
Gang Zhang and Dong Wang contributed to hypothesis development and manuscript preparation. Gang Zhang, and Jing Wang contributed to the study design. Jie Chen, and Mingyue Tong undertook data analyses. Xiaoping Gao, Jun Chang, and Gang Zhang drafted and revised the manuscript. All authors approved the final draft of the manuscript for publication.
CRediT authorship contribution statement
Gang Zhang: Writing – original draft, Investigation, Data curation. Dong Wang: Funding acquisition, Formal analysis, Data curation. Jie Chen: Validation, Supervision, Software. Mingyue Tong: Resources, Project administration, Methodology. Jing Wang: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision. Jun Chang: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision. Xiaoping Gao: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
The authors thank the staff and the participants of the NHANES study for their valuable contributions.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.pmedr.2024.102741.
Contributor Information
Jing Wang, Email: wj19880918@126.com.
Jun Chang, Email: changjun2008@hotmail.com.
Xiaoping Gao, Email: gxp678@163.com.
Appendix A. Supplementary data
The following are the Supplementary data to this article:
Data availability
The survey data are publicly available on the Internet for data users and researchers throughout the world https://www.cdc.gov/nchs/nhanes/.
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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
The survey data are publicly available on the Internet for data users and researchers throughout the world https://www.cdc.gov/nchs/nhanes/.