Skip to main content
Download PDF
Download ePub

Association between sleep duration and sleep quality with pre-sarcopenia in the 20–59-year-old population: evidence from the National Health and Nutrition Examination Surveys 2005–2014

Archives of Public Health volume 82, Article number: 162 (2024) Cite this article

Abstract

Background

Sarcopenia is a musculoskeletal disease characterized by a significant reduction in muscle mass, strength, and performance. As it mostly affects older adults, it is often recognized as a disease of old age. However, sleep is also closely related to its development. Hence, it becomes critical to explore the relationship between sleep and sarcopenia in populations under 60 years of age to develop strategies for preventing sarcopenia. We here aim to explore the specific association between sleep duration and sleep quality with pre-sarcopenia in the non-elderly population using large population samples.

Methods

This study involved 7,187 participants aged 20–59 years from the National Health and Nutrition Examination Survey (NHANES) conducted between 2005 and 2014. Pre-sarcopenia is defined based on the appendicular skeletal muscle mass (ASM) adjusted for body mass index (BMI). Self-reported sleep duration was categorized into three groups: <6 h (short sleep), 6–8 h (normal sleep), and > 8 h (long sleep). Sleep quality was assessed based on the Sleep Disorder and Trouble Sleeping Questionnaire. Univariate analysis and multivariate logistic regression were used to examine the relationship between sleep duration and sleep quality with pre-sarcopenia.

Results

Sleep quality was significantly linked with the risk of pre-sarcopenia (OR 1.72, 95% CI 1.36–2.18, P < 0.01). Longer or shorter sleep duration did not affect the risk of pre-sarcopenia, in contrast to normal sleep duration. Subgroup analysis demonstrated a more pronounced association in individuals who are > 40 years old (P < 0.01), non-Hispanic (P ≤ 0.01), overweight (P < 0.01), have a higher income (P < 0.01), and are more educated (P ≤ 0.01). Moreover, this association was noted in populations with or without smoking (P < 0.01) and alcohol consumption (P < 0.01), hypertension (P < 0.01) and diabetes (P ≤ 0.02).

Conclusion

Sleep quality is associated with an increased risk of pre-sarcopenia, while sleep duration is not in the population aged 20–59 years. Further prospective cohort studies with a large sample size are needed to determine causality and develop effective interventions for preventing sarcopenia in the population aged 20–59 years.

Peer Review reports

Text box 1. Contributions to the literature

• Currently, there is limited public health policy attention given to the relationship between sleep and pre-sarcopenia, particularly regarding sleep quality.

• This study found that poor sleep quality significantly increases the prevalence of pre-sarcopenia in adults aged 20–59, while there is no significant association between sleep duration and pre-sarcopenia.

• Public health policies should prioritize improving sleep quality over merely focusing on sleep duration to more effectively prevent pre-sarcopenia in adults aged 20–59.

Background

Sleep deprivation and poor sleep quality have become common health disorders in modern society [1]. According to a recent study, nearly 27.1% of Americans suffer from sleep disorders [2]. Sleep disorders are strongly associated with chronic diseases, such as hypertension, diabetes, cardiovascular disease, and certain types of cancers [3,4,5,6]. To maintain good physical and mental health, the Sleep Research Society and the American Academy of Sleep Medicine recommend that adults should sleep 7 h every night [7, 8]. However, sleep involves more than just its duration, including both quantitative and qualitative aspects [9]. The quality of sleep, including sleep continuity, and the circadian rhythm of sleep are critical for maintaining overall health [10]. In recent years, the relationship between sleep and sarcopenia has become a research hotspot [11, 12].

Sarcopenia is an age-related disorder characterized by an increasing decline in muscle mass. It creates an enormous clinical challenge for patients and burdens their daily lives [13]. Evidence suggests that people with sarcopenia have a threefold increased risk of death, experience functional decline, have fall-related injuries and hospitalizations, and are less likely to be satisfied with their lives [14, 15]. Further, sarcopenia is associated with higher hospitalization costs [16,17,18].

Pre-sarcopenia is a condition characterized by low skeletal muscle mass but normal muscle functions [19]. Sarcopenia is associated with a decline in skeletal muscle strength, accompanied by a decline in muscle mass. Sarcopenia is a disorder generally found in the older population. However, modern lifestyles have gradually led to an increase in its prevalence among young adults [20]. Therefore, it has become highly desirable to develop preventive strategies for young adults with a high risk of sarcopenia [21].

The accelerated pace of modern life has seriously challenged the duration and quality of sleep in young adults. Studies have shown that middle-aged adults with delayed sleep timings are more likely to suffer from sarcopenia [22, 23]. Although several studies have found a association between sleep duration and sarcopenia, there is some controversy surrounding this finding. Two community-based studies have shown that a sleep duration of less than 6 h or more than 8 h is associated with a higher risk of sarcopenia [24, 25]. In contrast, Kim et al. reported that sarcopenia among women is associated with higher sleep duration only [26, 27]. Therefore, understanding the role of sleep in pre-sarcopenia in the population aged 20–59 years is essential for developing relevant strategies to minimize the occurrence of sarcopenia. Thus, the primary objective of the present study is to examine the relationship between sleep duration and sleep quality with pre-sarcopenia in the population aged 20–59 years using a nationally representative sample in the United States.

Methods

Study design and participants

Data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2005 and 2014 were cross-sectionally analyzed. The study was approved by the National Center for Health Statistics and the NCHS Research Ethics Review Board (https://www.cdc.gov/nchs/nhanes/irba98.htm). All study participants provided written informed consent. This study was performed in accordance with the tenets of the Declaration of Helsinki and the relevant guidelines.

The original data set included 14,546 participants. We excluded 7,359 participants because of the missing data on pre-sarcopenia, sleep, and covariates; higher age (≥ 60 years); pregnant participants; and those weighing over 204 kg or taller than 195 cm. Our final analysis included 7,187 participants (Fig. 1).

Fig. 1
figure 1

Schematic of specific patient screening process

Full size image

Measurements and definition of pre-sarcopenia

From 2005 to 2014, NHANES data files on dual-energy X-ray absorptiometry (DXA) were collected to obtain complete results. DXA measurements of body composition were performed using the Hologic QDR-4500 A fanbeam densitometer (Hologic, Inc., Bedford, MA, USA). Clinical practices measure skeletal muscle mass in the legs and arms as a whole, typically known as appendicular skeletal muscle mass (ASM). Skeletal muscle mass index (SMI) was calculated by adjusting the ASM for BMI based on the recommendations of the Foundation for the National Institutes of Health Sarcopenia [28, 29]. Pre-sarcopenia is defined in men as having an SMI of < 0.789 and in women as having an SMI of < 0.512 [30].

Assessment of sleep quality

We measured sleep status using three questions, as in previous studies [31, 32]. Participants who responded “yes” to the question “Have you ever told a doctor or other health professional that you have trouble sleeping or a sleep disorder?” were identified as having a sleep trouble or sleep disorder. Next, participants were asked, “How much sleep do you get (hours)?” to identify their sleep duration. Sleep duration was classified as short (< 6 h/day), normal (6–8 h/day), and long (> 8 h/day) [33]. Sleep was evaluated based on two dimensions: sleep quality and sleep duration [34]. Participants without any sleep trouble and sleep disorder were defined as having good quality sleep, while those with sleep trouble or sleep disorder were defined as having poor quality sleep.

Covariates

A clinical experience-based and literature-based definition of the covariates was used, which included the following: sex (male or female); ethnicity (non-Hispanic White, non-Hispanic Black, Mexican American, and other); family income (≤$20,000 or >$20,000); educational level (< high school, high school, or > high school); living with a partner (yes or no); current smoking status (no or yes) [35]; current alcohol use (no or yes) [36]; presence of hypertension (no or yes) [37]; and presence of diabetes mellitus (DM) (no or yes) [38].

Statistical analysis

An unweighted frequency and a weighted estimated population proportion or weighted standard error (SE) were calculated for each variable. A t-test or chi-square test was performed for both continuous and categorical data. Univariate analysis was used to assess the association between sleep duration and sleep quality with pre-sarcopenia across sex-specific populations. Stratified analysis and adjusted multivariable regression analysis were performed to explore associations between sleep quality and the pre-sarcopenia risk. Statistical significance was defined as a P-value of 0.05 for all analyses performed using R 4.1.2.

Results

Participant characteristics

Baseline characteristics of the participants with pre-sarcopenia are presented in Table 1. In total, 7,187 participants were included in the study, of whom 555 (273 females [49.2%]) were diagnosed with pre-sarcopenia. As expected, the mean age and SE of the pre-sarcopenia group was 43.64 ± 0.58, while that for participants without pre-sarcopenia was 39.00 ± 0.31. In addition, participants with pre-sarcopenia had higher levels of BMI, lower levels of education, and lower levels of annual family income compared to those of participants without pre-sarcopenia (P < 0.01). In addition, the pre-sarcopenia group had a lower proportion of current smokers (P = 0.02) and alcoholic drinkers (P < 0.01) than those in the non-pre-sarcopenia group. Individuals with poor sleep quality have an increased prevalence of pre-sarcopenia (P < 0.01), as evidenced by the prevalence of sleep disorders (P < 0.01) and trouble sleeping (P < 0.01). However, no significant association was noted between pre-sarcopenia and sleep duration (P = 0.06).

Table 1 Characteristics of adults with and without pre-sarcopenia (N = 7187)
Full size table

Univariate analysis to determine the prevalence of pre-sarcopenia based on sleep duration and sleep quality

Univariate analysis was performed to assess the effect of sleep duration and sleep quality on pre-sarcopenia. Table 2 shows that poor sleep quality significantly increases the prevalence of pre-sarcopenia by 1.94 times (P < 0.01). Furthermore, sleep disorder and trouble sleeping significantly increased the prevalence of pre-sarcopenia by 2.93 and 1.77 times, respectively (P < 0.01). However, both < 6 h (P = 0.07) as well as > 8 h (P = 0.08) of sleep did not significantly increase the prevalence of pre-sarcopenia compared to that observed with normal sleep duration (6–8 h) (Table 3). Similarly, both long and short sleep durations were not significantly associated with pre-sarcopenia, with these associations being consistent across both males and females (P > 0.05).

Table 2 Univariate analysis for assessing the association between sleep quality and pre-sarcopenia
Full size table
Table 3 Univariate analysis for assessing the association between sleep duration and pre-sarcopenia
Full size table

Multivariate logistic regression of the relationship between pre-sarcopenia and sleep duration and sleep quality

We established logistic regression models to analyze the relationship between pre-sarcopenia and sleep quality (Table 4). After adjustment for age, BMI, annual family income, ethnicity, education, smoking, drinking, hypertension, and diabetes, the prevalence of pre-sarcopenia in the group with poor sleep quality was 1.72 times than in the good sleep quality group (P < 0.01). Furthermore, subgroup analyses showed that sleep disorders and trouble sleeping significantly increased the prevalence of pre-sarcopenia by 1.91 and 1.60 times, respectively (P < 0.01).

Table 4 Adjusted multivariable logistic regression analysis for assessing the association between sleep quality and pre-sarcopenia
Full size table

Stratified analysis association between pre-sarcopenia and sleep quality

Stratified analyses were performed based on sex, age, ethnicity, BMI, annual family income, educational level, marital status, hypertension, diabetes, smoking status, and drinking status to investigate the link between sleep quality and pre-sarcopenia in diverse populations (Table 5). We found significant interactions among age, sex, ethnicity, and education level in the relationship between sleep quality and sarcopenia (P for interaction = 0.05, < 0.01, 0.01, and 0.03, respectively). A significant association effect of sleep quality with pre-sarcopenia was noted in individuals aged 40–50 years (P < 0.01) as well as in those aged 50–60 years (P = 0.03). Individuals with or without comorbid conditions, such as hypertension, diabetes, smoking, or drinking, can also exhibit this significant interaction (P < 0.01).

Table 5 Association between sleep quality and pre-sarcopenia according to different variables
Full size table

In addition, significant interactions were found between the annual family income and the association between sleep disorder and pre-sarcopenia (P for interaction = 0.02, Supplementary Table 1). Age and ethnicity were also found to be significantly interactions with the association between trouble sleeping and sarcopenia (P for interaction = 0.05, Supplementary Table 2). Moreover, both indicators (sleep disorder and trouble sleeping) were found to be associated with pre-sarcopenia in individuals aged 40–50 years, overweight individuals, those with a higher annual income, with these associations being consistent across both males and females.

Discussion

This cross-sectional study was conducted based on the survey data collected over 10 years in the United States. This research showed that sleep quality (aOR = 1.72, 95CI%: 1.36–2.18) is associated with pre-sarcopenia in the population aged 20–59 years. No significant association was noted between long sleep duration (> 8 h) or short sleep duration (< 6 h) with pre-sarcopenia compared to those with normal sleep duration (6–8 h) in the abovementioned population.

Long sleep duration plays an important role in the development of sarcopenia. A meta-analysis demonstrated that sleep duration is associated with obesity, cardiovascular disease, diabetes mellitus, and mortality from all causes, compared to normal sleep duration [39]. Our results showed that the prevalence of pre-sarcopenia was higher among participants with a long sleep duration compared to those with normal sleep duration (9.05% vs. 6.70%). Moreover, participants who slept long hours had a 1.39 times higher prevalence of pre-sarcopenia than those who slept normal hours. However, there was no statistically significant difference between long sleep duration and pre-sarcopenia in the 20–59-year-old population. Hu et al. conducted a study on people aged more than 60 years in Chinese communities and demonstrated that sarcopenia was associated with long sleep duration only in women. They did not report any significant difference among men [24]. However, no such differences could be seen in our further univariate analysis. The direct mechanism of the association between long sleep duration and sarcopenia remains unclear, although several studies have suggested various potential mechanisms. Fielding et al. found that increasing sleep duration could decrease physical activity, which could, in turn, lead to the development of sarcopenia [40]. Furthermore, Gildner carried out a longitudinal study that showed that prolonged sleep may impair cognitive functions, resulting in various physical problems, such as reduced lower limb strength and gait dysfunction [41]. Alternatively, the results of a recent cross-sectional study showed that individuals who sleep > 9 h/day are at an increased risk of sarcopenia owing to obesity [42]. Research conducted in mouse models suggests that obesity increases the levels of pro-inflammatory cytokines, which could be an important contributor to sarcopenia [43]. Notably, both Asian and Caucasian participants exhibited a significant association between longer sleep duration and a high sarcopenia risk, indicating that sleep duration is not associated with genetic susceptibility [11].

Short sleep duration is also associated with serious health problems, such as obesity, diabetes, hypertension, and cardiovascular disease [44]. Short sleep duration is linked with a greater prevalence of pre-sarcopenia than normal sleep duration (8.38% vs. 6.70%). After correcting for multiple influencing factors, reduced sleep duration was not found to be significantly associated with the risk of pre-sarcopenia in the 20–59-year-old population. A dose–response meta-analysis showed a U-shaped association between sleep duration and sarcopenia. Participants who slept 6 h had an increased risk of sarcopenia [45]. Similarly, Chien et al. found a U-shaped relationship between sleep duration and sarcopenia, and that the risk of sarcopenia was three times greater in those who slept < 6 h than in those who slept 6–8 h [25]. Several pathophysiologic mechanisms may mediate the link between sleep deprivation and sarcopenia. Animal study has shown that shorter sleep duration increases cortisol levels and causes low-level inflammation, resulting in oxidative and proteolytic pathways that cause sarcopenia [46]. Alternatively, shortened sleep duration may induce insulin resistance through multiple metabolic pathways, which may damage muscle elasticity and accelerate muscle deterioration, leading to sarcopenia [47].

Compared to sleep duration, sleep quality plays a more important role in the development of various systemic diseases. For example, a study on hearing loss showed that only the quality of sleep is associated with hearing loss, but not the duration of sleep [48]. Sleep quality has been shown to be associated with sarcopenia or its influencing factors in the elderly population [49]. Our finding also revealed similar results, that is, pre-sarcopenia has a significant association with sleep quality but not with sleep duration in people aged 20–59 years. Simultaneously, the risk of sarcopenia in participants with poor sleep quality was 1.72 times higher than in those with good sleep quality. Shibuki et al. demonstrated that insomnia is significantly associated with sarcopenia, with a 1.67 times risk than that of normal sleepers [33]. During regular sleep, body damage is gradually repaired and rejuvenated, such as protein synthesis and hormone production, all of which contribute to muscle growth [50]. However, when the circadian rhythm is disrupted, the body exhibits a chronic inflammatory state. As a result, protein synthesis in the muscle is reduced, which causes hydrolysis of muscle protein, resulting in sarcopenia [51].

The results of a Spanish cohort of middle-aged and older adults showed that neither sleep duration nor sleep quality were associated with sarcopenia, and only depression was associated with it [52]. However, a Chinese study showed a significant correlation between depression and sarcopenia, and subgroup analyses showed that sleeping less than 8 h increased the risk [53]. Depression is associated with reduced physical activity and changes in muscle metabolism, increasing the risk of sarcopenia is well established [54, 55]. However, differences in the role of sleep in the relationship between depression and sarcopenia may be the result of different populations and differences in sample size, and further research is still needed to elucidate.

Gender differences in sarcopenia are also of concern. Kwon et al. conducted a cohort study in Korea and reported a prevalence of 14.3% for sarcopenia. It also reported that men had a higher prevalence of sarcopenia (18.7%) than women (9.7%) [27]. In addition, our study showed a higher prevalence of pre-sarcopenia in men compared to women, consistent with previous studies. This result could be explained by the fact that men have a higher muscle mass than women, although the magnitude of muscle loss with age is greater for men than it is for women [56]. In contrast, an over-69-year-old Spanish study reported a higher prevalence of sarcopenia in women than in men, possibly because of differences in the prevalence of sarcopenia at different age intervals [56]. This aspect needs to be further investigated.

Our results show that pre-sarcopenia is associated with alcohol consumption, which is consistent with previous findings [57]. The elevated prevalence of pre-sarcopenia among individuals in the alcohol consumption group could be attributed to the impact of alcohol on skeletal muscle. Studies in vivo suggest that alcohol may affect the synthesis of muscle proteins and impair their function, and that excessive alcohol intake is a lifestyle that may lead to sarcopenia [58].

In the present study, we showed that a large sample size allows for a more representative and comprehensive analysis of the relationship between sleep and the prevalence of pre-sarcopenia. The study on the independent effect of sleep on the prevalence of pre-sarcopenia can be made more reliable by adjusting for various confounding variables. However, there are potential limitations to consider. First, the study participants were recruited from specific health centers in the United States. Therefore, the results may not be relevant to other populations. Second, the study design was based on a cross-sectional analysis, which did not allow for the determination of a causal relationship between sleep and pre-sarcopenia. More longitudinal research is needed to clarify the causal relationships between sleep and pre-sarcopenia. Third, self-reported sleep duration is likely to be subject to recall bias and therefore not very precise. Fourth, physical activity, as well as medication history, is well known to affect sleep quality and sarcopenia risk. However, these variables were not controlled for during the study. Future studies should take these limitations into account when investigating the relationship between sleep and pre-sarcopenia.

Conclusion

Our research provided evidence that poor sleep quality, but not sleep duration, is associated with an increased risk of pre-sarcopenia in people aged 20–59 years. Further prospective cohort studies with a large sample size are needed to determine causality and develop effective interventions for the prevention of sarcopenia among 20–59-year-old people.

Data availability

The data sets generated during the current study are available from the corresponding author on reasonable request.

Abbreviations

ASM:

Appendicular skeletal muscle mass

NHANES:

National Health and Nutrition Examination Survey

SE:

Standard error

SMI:

Skeletal muscle mass index

References

  1. Zhang Y, Chen C, Lu L, Knutson KL, Carnethon MR, Fly AD, Luo J, Haas DM, Shikany JM, Kahe K. Association of magnesium intake with sleep duration and sleep quality: findings from the CARDIA study. Sleep. 2022;45(4).

  2. Kase BE, Liu J, Wirth MD, Shivappa N, Hebert JR. Associations between dietary inflammatory index and sleep problems among adults in the United States, NHANES 2005–2016. Sleep Health. 2021;7(2):273–80.

    Article  PubMed  Google Scholar 

  3. Li C, Shang S. Relationship between sleep and hypertension: findings from the NHANES (2007–2014). Int J Environ Res Public Health. 2021;18(15).

  4. Antza C, Kostopoulos G, Mostafa S, Nirantharakumar K, Tahrani A. The links between sleep duration, obesity and type 2 diabetes mellitus. J Endocrinol. 2021;252(2):125–41.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Fan M, Sun D, Zhou T, Heianza Y, Lv J, Li L, Qi L. Sleep patterns, genetic susceptibility, and incident cardiovascular disease: a prospective study of 385 292 UK biobank participants. Eur Heart J. 2020;41(11):1182–9.

    Article  PubMed  Google Scholar 

  6. Mogavero MP, DelRosso LM, Fanfulla F, Bruni O, Ferri R. Sleep disorders and cancer: state of the art and future perspectives. Sleep Med Rev. 2021;56:101409.

    Article  PubMed  Google Scholar 

  7. Watson NF, Badr MS, Belenky G, Bliwise DL, Buxton OM, Buysse D, Dinges DF, Gangwisch J, Grandner MA, Kushida C, et al. Recommended amount of sleep for a healthy adult: a joint consensus statement of the American Academy of Sleep Medicine and Sleep Research Society. Sleep. 2015;38(6):843–4.

    PubMed  PubMed Central  Google Scholar 

  8. Watson NF, Badr MS, Belenky G, Bliwise DL, Buxton OM, Buysse D, Dinges DF, Gangwisch J, Grandner MA, Kushida C, et al. Recommended amount of sleep for a healthy adult: a joint consensus statement of the American Academy of Sleep Medicine and Sleep Research Society. J Clin Sleep Med. 2015;11(6):591–2.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Fernandez-Mendoza J, Vgontzas AN, Liao D, Shaffer ML, Vela-Bueno A, Basta M, Bixler EO. Insomnia with objective short sleep duration and incident hypertension: the Penn State cohort. Hypertension. 2012;60(4):929–35.

    Article  CAS  PubMed  Google Scholar 

  10. Buysse DJ. Sleep health: can we define it? Does it matter? Sleep. 2014;37(1):9–17.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Li X, He J, Sun Q. Sleep duration and sarcopenia: an updated systematic review and meta-analysis. J Am Med Dir Assoc. 2023;24(8):1193–e12061195.

    Article  PubMed  Google Scholar 

  12. Yuan S, Larsson SC. Epidemiology of sarcopenia: prevalence, risk factors, and consequences. Metabolism. 2023;144:155533.

    Article  CAS  PubMed  Google Scholar 

  13. Rosenberg IH. Sarcopenia: origins and clinical relevance. J Nutr. 1997;127(5 Suppl):s990–1.

    Article  Google Scholar 

  14. Kamel HK. Sarcopenia and aging. Nutr Rev. 2003;61(5 Pt 1):157–67.

    Article  PubMed  Google Scholar 

  15. Beaudart C, Demonceau C, Reginster JY, Locquet M, Cesari M, Cruz Jentoft AJ, Bruyère O. Sarcopenia and health-related quality of life: a systematic review and meta-analysis. J Cachexia Sarcopenia Muscle. 2023;14(3):1228–43.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Sousa AS, Guerra RS, Fonseca I, Pichel F, Ferreira S, Amaral TF. Financial impact of sarcopenia on hospitalization costs. Eur J Clin Nutr. 2016;70(9):1046–51.

    Article  CAS  PubMed  Google Scholar 

  17. Ethgen O, Beaudart C, Buckinx F, Bruyère O, Reginster JY. The future prevalence of sarcopenia in Europe: a claim for public health action. Calcif Tissue Int. 2017;100(3):229–34.

    Article  CAS  PubMed  Google Scholar 

  18. Bruyère O, Beaudart C, Ethgen O, Reginster JY, Locquet M. The health economics burden of sarcopenia: a systematic review. Maturitas. 2019;119:61–9.

    Article  PubMed  Google Scholar 

  19. Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, Martin FC, Michel JP, Rolland Y, Schneider SM, et al. Sarcopenia: European consensus on definition and diagnosis: report of the European Working Group on sarcopenia in older people. Age Ageing. 2010;39(4):412–23.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Xu J, Han X, Chen Q, Cai M, Tian J, Yan Z, Guo Q, Xu J, Lu H. Association between sarcopenia and prediabetes among non-elderly US adults. J Endocrinol Invest. 2023;46(9):1815–24.

    Article  CAS  PubMed  Google Scholar 

  21. Li R, Lin S, Tu J, Chen Y, Cheng B, Mo X, Xie T. Establishment and evaluation of a novel practical tool for the diagnosis of pre-sarcopenia in young people with diabetes mellitus. J Transl Med. 2023;21(1):393.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Lucassen EA, de Mutsert R, le Cessie S, Appelman-Dijkstra NM, Rosendaal FR, van Heemst D, den Heijer M, Biermasz NR. Poor sleep quality and later sleep timing are risk factors for osteopenia and sarcopenia in middle-aged men and women: the NEO study. PLoS ONE. 2017;12(5):e0176685.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Yu JH, Yun CH, Ahn JH, Suh S, Cho HJ, Lee SK, Yoo HJ, Seo JA, Kim SG, Choi KM, et al. Evening chronotype is associated with metabolic disorders and body composition in middle-aged adults. J Clin Endocrinol Metab. 2015;100(4):1494–502.

    Article  CAS  PubMed  Google Scholar 

  24. Hu X, Jiang J, Wang H, Zhang L, Dong B, Yang M. Association between sleep duration and sarcopenia among community-dwelling older adults: a cross-sectional study. Med (Baltim). 2017;96(10):e6268.

    Article  Google Scholar 

  25. Chien MY, Wang LY, Chen HC. The relationship of sleep duration with obesity and sarcopenia in community-dwelling older adults. Gerontology. 2015;61(5):399–406.

    Article  PubMed  Google Scholar 

  26. Kim RH, Kim KI, Kim JH, Park YS. Association between sleep duration and body composition measures in Korean adults: the Korea National Health and Nutrition Examination Survey 2010. Korean J Fam Med. 2018;39(4):219–24.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Kwon YJ, Jang SY, Park EC, Cho AR, Shim JY, Linton JA. Long sleep duration is associated with sarcopenia in Korean adults based on data from the 2008–2011 KNHANES. J Clin Sleep Med. 2017;13(9):1097–104.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Cawthon PM, Peters KW, Shardell MD, McLean RR, Dam TT, Kenny AM, Fragala MS, Harris TB, Kiel DP, Guralnik JM, et al. Cutpoints for low appendicular lean mass that identify older adults with clinically significant weakness. J Gerontol Biol Sci Med Sci. 2014;69(5):567–75.

    Article  Google Scholar 

  29. Kim KM, Jang HC, Lim S. Differences among skeletal muscle mass indices derived from height-, weight-, and body mass index-adjusted models in assessing sarcopenia. Korean J Intern Med. 2016;31(4):643–50.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Golabi P, Gerber L, Paik JM, Deshpande R, de Avila L, Younossi ZM. Contribution of sarcopenia and physical inactivity to mortality in people with non-alcoholic fatty liver disease. JHEP Rep. 2020;2(6):100171.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Zhang J, Yu S, Zhao G, Jiang X, Zhu Y, Liu Z. Associations of chronic diarrheal symptoms and inflammatory bowel disease with sleep quality: a secondary analysis of NHANES 2005–2010. Front Neurol. 2022;13:858439.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Lei X, Xu Z, Chen W. Association of oxidative balance score with sleep quality: NHANES 2007–2014. J Affect Disord. 2023;339:435–42.

    Article  CAS  PubMed  Google Scholar 

  33. Shibuki T, Iida M, Harada S, Kato S, Kuwabara K, Hirata A, Sata M, Matsumoto M, Osawa Y, Okamura T, et al. The association between sleep parameters and sarcopenia in Japanese community-dwelling older adults. Arch Gerontol Geriatr. 2023;109:104948.

    Article  PubMed  Google Scholar 

  34. Lee CL, Tzeng HE, Liu WJ, Tsai CH. A cross-sectional analysis of the association between sleep duration and osteoporosis risk in adults using 2005–2010 NHANES. Sci Rep. 2021;11(1):9090.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Hou W, Chen S, Zhu C, Gu Y, Zhu L, Zhou Z. Associations between smoke exposure and osteoporosis or osteopenia in a US NHANES population of elderly individuals. Front Endocrinol (Lausanne). 2023;14:1074574.

    Article  PubMed  Google Scholar 

  36. Zhou Z, Huang Z, Ai G, Guo X, Zeng G, Zhu W. Association between alcohol consumption and kidney stones in American adults: 2007–2016 NHANES. Front Public Health. 2023;11:1156097.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Yuan M, He J, Hu X, Yao L, Chen P, Wang Z, Liu P, Xiong Z, Jiang Y, Li L. Hypertension and NAFLD risk: insights from the NHANES 2017–2018 and Mendelian randomization analyses. Chin Med J (Engl). 2024;137(4):457–64.

    Article  PubMed  Google Scholar 

  38. Palmer MK, Toth PP. Trends in lipids, obesity, metabolic syndrome, and diabetes mellitus in the United States: an NHANES analysis (2003–2004 to 2013–2014). Obes (Silver Spring). 2019;27(2):309–14.

    Article  CAS  Google Scholar 

  39. Jike M, Itani O, Watanabe N, Buysse DJ, Kaneita Y. Long sleep duration and health outcomes: a systematic review, meta-analysis and meta-regression. Sleep Med Rev. 2018;39:25–36.

    Article  PubMed  Google Scholar 

  40. Fielding RA, Vellas B, Evans WJ, Bhasin S, Morley JE, Newman AB, van Abellan G, Andrieu S, Bauer J, Breuille D, et al. Sarcopenia: an undiagnosed condition in older adults. Current consensus definition: prevalence, etiology, and consequences. International working group on Sarcopenia. J Am Med Dir Assoc. 2011;12(4):249–56.

    Article  PubMed  Google Scholar 

  41. Gildner TE, Liebert MA, Kowal P, Chatterji S, Snodgrass JJ. Associations between sleep duration, sleep quality, and cognitive test performance among older adults from six middle income countries: results from the study on global ageing and adult health (SAGE). J Clin Sleep Med. 2014;10(6):613–21.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Lu L, He X, Song Y, Zhuang M, Wu X, Chen N. Prevalence and risk factors of sarcopenia without obesity and sarcopenic obesity among Chinese community older people in suburban area of Shanghai: a cross-sectional study. Front Aging Neurosci. 2022;14:1034542.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Ge Y, Li S, Yao T, Tang Y, Wan Q, Zhang X, Zhao J, Zhang M, Shao M, Wang L, et al. Promotion of healthy adipose tissue remodeling ameliorates muscle inflammation in a mouse model of sarcopenic obesity. Front Nutr. 2023;10:1065617.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Dashti HS, Scheer FA, Jacques PF, Lamon-Fava S, Ordovás JM. Short sleep duration and dietary intake: epidemiologic evidence, mechanisms, and health implications. Adv Nutr. 2015;6(6):648–59.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Pourmotabbed A, Ghaedi E, Babaei A, Mohammadi H, Khazaie H, Jalili C, Symonds ME, Moradi S, Miraghajani M. Sleep duration and sarcopenia risk: a systematic review and dose-response meta-analysis. Sleep Breath. 2020;24(4):1267–78.

    Article  PubMed  Google Scholar 

  46. Andersson DC, Betzenhauser MJ, Reiken S, Meli AC, Umanskaya A, Xie W, Shiomi T, Zalk R, Lacampagne A, Marks AR. Ryanodine receptor oxidation causes intracellular calcium leak and muscle weakness in aging. Cell Metab. 2011;14(2):196–207.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Abbatecola AM, Paolisso G, Fattoretti P, Evans WJ, Fiore V, Dicioccio L, Lattanzio F. Discovering pathways of sarcopenia in older adults: a role for insulin resistance on mitochondria dysfunction. J Nutr Health Aging. 2011;15(10):890–5.

    Article  CAS  PubMed  Google Scholar 

  48. Yévenes-Briones H, Caballero FF, Estrada-deLeón DB, Struijk EA, Mesas AE, Banegas JR, Rodríguez-Artalejo F, Lopez-García E. Duration and quality of sleep and risk of self-reported hearing loss: the UK Biobank study. Ear Hear. 2023;44(5):1182–9.

    PubMed  Google Scholar 

  49. Locquet M, Beaudart C, Delandsheere L, Reginster JY, Bruyère O. Subjective sleep quality among sarcopenic and non-sarcopenic older adults: results from the SarcoPhAge cohort. J Frailty Aging. 2018;7(3):176–81.

    CAS  PubMed  Google Scholar 

  50. Trommelen J, van Loon LJ. Pre-sleep protein ingestion to improve the skeletal muscle adaptive response to exercise training. Nutrients. 2016;8(12).

  51. Cesari M, Kritchevsky SB, Nicklas B, Kanaya AM, Patrignani P, Tacconelli S, Tranah GJ, Tognoni G, Harris TB, Incalzi RA, et al. Oxidative damage, platelet activation, and inflammation to predict mobility disability and mortality in older persons: results from the health aging and body composition study. J Gerontol Biol Sci Med Sci. 2012;67(6):671–6.

    Article  Google Scholar 

  52. Fábrega-Cuadros R, Cruz-Díaz D, Martínez-Amat A, Aibar-Almazán A, Redecillas-Peiró MT, Hita-Contreras F. Associations of sleep and depression with obesity and sarcopenia in middle-aged and older adults. Maturitas. 2020;142:1–7.

    Article  PubMed  Google Scholar 

  53. Zhong Q, Jiang L, An K, Zhang L, Li S, An Z. Depression and risk of sarcopenia: a national cohort and Mendelian randomization study. Front Psychiatry. 2023;14:1263553.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Hayashi T, Umegaki H, Makino T, Cheng XW, Shimada H, Kuzuya M. Association between sarcopenia and depressive mood in urban-dwelling older adults: a cross-sectional study. Geriatr Gerontol Int. 2019;19(6):508–12.

    Article  PubMed  Google Scholar 

  55. Chen X, Guo J, Han P, Fu L, Jia L, Yu H, Yu X, Hou L, Wang L, Zhang W, et al. Twelve-month incidence of depressive symptoms in suburb-dwelling Chinese older adults: role of sarcopenia. J Am Med Dir Assoc. 2019;20(1):64–9.

    Article  PubMed  Google Scholar 

  56. Salvà A, Serra-Rexach JA, Artaza I, Formiga F, Rojano ILX, Cuesta F, López-Soto A, Masanés F, Ruiz D, Cruz-Jentoft AJ. Prevalence of sarcopenia in Spanish nursing homes: comparison of the results of the ELLI study with other populations. Rev Esp Geriatr Gerontol. 2016;51(5):260–4.

    Article  PubMed  Google Scholar 

  57. Fanelli Kuczmarski M, Mason MA, Beydoun MA, Allegro D, Zonderman AB, Evans MK. Dietary patterns and sarcopenia in an urban African American and White population in the United States. J Nutr Gerontol Geriatr. 2013;32(4):291–316.

    Article  PubMed  Google Scholar 

  58. Rom O, Kaisari S, Aizenbud D, Reznick AZ. Lifestyle and sarcopenia-etiology, prevention, and treatment. Rambam Maimonides Med J. 2012;3(4):e0024.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank Bullet Edits Limited for the linguistic editing and proofreading of the manuscript.

Funding

This study did not receive any funding and there are no financial conflicts of interest.

Author information

Authors and Affiliations

  1. Department of Clinical Nutrition, The Affiliated Huaian No.1 People’s Hospital of Nanjing Medical University, 1 Huanghe West Road, Huaian, 223300, Jiangsu Province, China

    Xiuxun Dong & Yang Shen

  2. National & Local Joint Engineering Research Center for Mineral Salt Deep Utilization, Huaiyin Institute of Technology, Huaian, 223003, Jiangsu Province, China

    Lei He

  3. Department of Clinical Nutrition, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000, Shandong Province, China

    Li Zhang

Authors
  1. Xiuxun Dong
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Lei He
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Li Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Yang Shen
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Yang Shen drafted the manuscript. Xiuxun Dong designed the study. Lei He and Li Zhang analyzed the data. All authors read and approved the final manuscript for publication.

Corresponding author

Correspondence to Yang Shen.

Ethics declarations

Ethics approval and consent to participate

The Research Ethics Review Committee approved the NHANES survey protocol. Institutional Review Board approval and documented consent was obtained from participants (https://www.cdc.gov/nchs/nhanes/irba98.htm).

Consent for publication

All authors of the manuscript have read and agreed to its content and are accountable for all aspects of the accuracy and integrity of the manuscript in accordance with ICMJE (International Committee of Medical Journal Editors) criteria.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary Material 2

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dong, X., He, L., Zhang, L. et al. Association between sleep duration and sleep quality with pre-sarcopenia in the 20–59-year-old population: evidence from the National Health and Nutrition Examination Surveys 2005–2014. Arch Public Health 82, 162 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13690-024-01394-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13690-024-01394-2

Keywords

Archives of Public Health

ISSN: 2049-3258