Correlation between sleep disorder, anxiety, depression, and sarcopenia in multiethnic areas of western China

Correlation between sleep disorder, anxiety, depression, and sarcopenia in multiethnic areas of western China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Correlation between sleep disorder, anxiety, depression, and sarcopenia in multiethnic areas of western China Zhigang Xu, Xiaolei Liu, Huang Ning, Gongchang Zhang, Shuli Jia, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4370867/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Sarcopenia not only leads to impaired physical function but may also be associated with changes in sleep and mental health as individuals age. Research on the relationship between sleep, anxiety, and depression and adultonset sarcopenia is limited; however, there are no reports indicating the relationship between them and the different groups of sarcopenia. The aim of this study is to explore the correlation between sarcopenia (diagnosed sarcopenia, severe sarcopenia) and sleep, anxiety, and depression in different groups in the multiethnic region of western China based on the 2019 Asian sarcopenia diagnostic criteria. Methods The diagnostic method recommended by the Asian Working Group for Sarcopenia in 2019 was used to screen for sarcopenia. The population in the multiethnic region of western China included in this study underwent bioelectrical impedance analysis to classify sarcopenia into the diagnosed sarcopenia and severe sarcopenia groups, while also recording other data for analysis. The Pittsburgh Sleep Quality Index, the 7-item Generalized Anxiety Disorder Questionnaire, and the 15-item geriatric depression scale were used to assess the sleep quality, anxiety, and depression status of participants, respectively. Multiple logistic regression multivariate analysis was used to determine the relationship among sleep, anxiety, depression, and the different types of sarcopenia. Results Among the 4500 participants surveyed in the western region of China, 408 (9.06%) were identified as having myasthenia gravis and 618 (13.73%) as having severe myasthenia gravis, whereas 2015 individuals (44.78%) had poor sleep quality, 842 (18.71%) had anxiety, and 1045 (23.22%) had depression. Sleep abnormalities were associated with severe sarcopenia (odds ratio [OR]: 0.717, 95% confidence interval [CI] 0.550–0.934), whereas depression was associated with diagnosed sarcopenia (OR: 1.289, 95%CI 1.032–1.608) and severe sarcopenia (OR: 1.622, 95%CI 1.032–1.608). Conclusion The western region of China is a multiethnic area with 44.78% of participants > 50 years of age experiencing poor sleep quality, 18.71% suffering from anxiety, and 23.22% experiencing depression. It may be possible to delay or reduce the severity of sarcopenia by early intervention in improving sleep quality and alleviating depression. Clinical trial number : ChiCTR1800018895 Sarcopenia Western China Multiethnic Sleep quality Anxiety Depression Figures Figure 1 Background The aging population of China is on the rise. By the year 2050, the number of individuals in China over 65 years of age is projected to reach 400 million, including 150 million people over 80 years of age, along with an increase in the public medical and health burden [ 1 ]. Therefore, age-related diseases have attracted increasing attention from society. With an increase in age, the functions of the human body gradually decline when individuals reach the middle-age stage [ 2 ]. Certain groups of people, especially the elderly, may experience an increased incidence of sleep disorders, anxiety, and even depression [ 3 – 5 ]. These psychological and mental changes may also bring about changes in hormone levels and directly affect protein synthesis in the body. Some of these proteins may be key in maintaining muscle mass [ 6 ]. At the same time, the occurrence of sleep, anxiety, and even depression may alter daily life and diet [ 7 ] and cause changes in muscle metabolism. As a progressive and systemic disease of the skeletal muscle, sarcopenia occurs with aging and is associated with a high rate of adverse outcomes [ 8 ]. Hospitalization rates and adverse outcomes, including death, are associated with sarcopenia. Older adults with severe muscle loss have an increased risk of shortterm mortality, making sarcopenia one of the predictors of mortality in community-dwelling older adults [ 9 ]. This also indicates that early intervention in the prevention and treatment of sarcopenia is particularly important for healthcare services in China. Poor sleep quality is associated with impaired physical function, mortality, frailty, and falls in older adults [ 10 – 12 ]. Sleep may impact muscle mass and strength through metabolism, hormones, and immune factors, which, in turn, may affect physical performance [ 13 ]. Anxiety is common in the elderly population; it is often a comorbidity with depression and is associated with cognitive decline [ 14 ]. Cognitive function is related to sarcopenia [ 15 ]. In ethnically diverse regions such as China, there is limited research on the association between sleep, anxiety, depression, and sarcopenia. Due to differences such as ethnicity, lifestyle, dietary habits, economy, geography, and beliefs between Western countries, such as Europe, and Asian countries, Europe and Asia have each established different working groups on sarcopenia. These groups have formulated assessment methods and diagnostic criteria for sarcopenia, each having its own variations. Consequently, there are certain disparities in the reported prevalence of regional sarcopenia among different countries. China is a populous country in Asia having a multiethnic population. It could therefore be of great significance to study the situation of sarcopenia in the multiethnic regions of western China. Sarcopenia screening in medical institutions or clinical research, as recommended by the Asian Working Group for Sarcopenia, can further be separated into diagnosed sarcopenia and severe sarcopenia [ 16 ]. Most studies only present the correlation between single sleep or depression factors and overall sarcopenia. Thus, there is a lack of research on the relationship between anxiety and sleep, depression, and different sarcopenia subgroups, as well as a dearth of definitive research conclusions. In this study, we used the Western China Health and Aging Trends (WCHAT) longitudinal multicenter cohort study [ 17 ]. The aim of this study was to understand the prevalence and risk factors of sleep disorders, anxiety, depression, and sarcopenia in the multiethnic population of western China and to analyze the relationship between sleep, anxiety, and depression and different sarcopenia subgroups. Methods Study design and participants The study population comprised individuals from multiple provinces and cities in the western region of China, representing various ethnic groups. This study is based on the ongoing prospective cohort study WCHAT and the research team has published a detailed description of the methodology and study design [ 17 ]. The Ethics Committee of West China Hospital, Sichuan University, China, reviewed and approved this study (reference number: 2017 − 445), and all participants signed the informed consent form [ 17 ]. The subjects included in this study were from 4 provinces in western China, including Sichuan, Xinjiang, Guizhou, and Yunnan, with the majority from Sichuan. The participants represent various ethnic groups such as Han, Tibetan, Qiang, Yi, Hui, Zhuang, and Miao. Inclusion criteria for participants: living in the region for ≥ 3 years; age ≥ 50 years; voluntary participation in the study. Exclusion criteria: expected lifespan < 6 months; acute diseases of important organs such as the heart, liver, kidneys, etc., and severe diseases such as respiratory failure; refusal to participate in the survey. Eventually, 7536 participants from the multiethnic regions in western China were recruited, and data on bioelectrical impedance analysis (BIA) were obtained from 4500 individuals. Information on sleep, anxiety, and depression scales was ultimately used for the analysis of individuals with muscle dystrophy. All personnel involved with data collection in this study received rigorous training, and health checks were conducted by relevant professional technicians. Measures Screening for sarcopenia Sarcopenia is characterized by the accelerated loss of muscle mass and function. For primary healthcare or community-based health purposes, the Asian Working Group on Sarcopenia (AWGS) has defined “possible sarcopenia” as low muscle strength or physical function [ 16 ]. In this study, the screening methods recommended by AWGS were followed, using the sarcopenia assessment pathways corresponding to clinical research, categorizing sarcopenia into diagnosed sarcopenia and severe sarcopenia. The specific assessment methods for obtaining relevant data are as stated below. In this study, we chose BIA for assessment of the muscle mass of the study population, and the InBody 770 body composition instrument was used for data collection. The reliability of this instrument was validated among the relevant population in China [ 18 , 19 ]. In accordance with the AWGS2019 recommendations, we used a cutoff value of 7.0 kg/m 2 for men and 5.7 kg/m 2 for women for skeletal muscle mass index (ASMI) [ 16 ]. Hand grip testing is used to reflect muscle strength. A dynamometer (EH101; Camry, Zhongshan) was used to measure the grip strength of the dominant hand of subjects. During the measurement, subjects were asked to stand with their feet naturally apart and arms hanging down, and to perform the maximum grip strength test on 2 separate occasions. The maximum value was recorded. The standard for weak grip strength is < 18 kg for females and < 28 kg for males [ 16 ]. The general gait speed test to measure walking speed requires participants to wear flat shoes and allows for the use of a walking aid. Participants can rest during the measurement but should not sit down. The AWGS recommends a critical value of 1 m/s for gait speed in individuals with muscle weakness [ 16 ]. Sleep quality assessment The Pittsburgh Sleep Quality Index (PSQI) is used to gauge sleep quality and serves as an indicator of subjective sleep quality for the past month. It consists of 19 items and is commonly used in the diagnosis of sleep disorders in both clinical and research settings, serving as a standardized assessment for patients with sleep difficulties [ 20 ]. PSQI scores ≥ 5 indicate poor sleep quality, whereas scores < 5 indicate good sleep quality. Anxiety assessment The Generalized Anxiety Disorder Questionnaire (GAD-7) is used to measure anxiety. Currently, GAD-7 is one of the most widely used anxiety assessment measures in clinical practice and research because of its high diagnostic reliability and efficiency [ 20 ]. Depression assessment To assess the symptoms of depression, the 15-item Geriatric Depression Scale was used, which was developed based on the unique symptoms such as somatic symptoms, anxiety, and cognitive decline commonly exhibited by older adults with depression. A score of ≥ 15 or ≥ 5 indicates depression [ 21 ]. General information on the study population This includes demographics (gender, age, ethnicity, marital status, employment status, living arrangement), lifestyle factors (alcohol consumption, smoking status), and health conditions (chronic diseases such as hypertension, diabetes, heart disease, chronic obstructive pulmonary disease). Statistical analysis The study population data were analyzed using SPSS 22.0. Continuous variables are expressed as mean ± standard deviation and t -test was used for group comparisons. Categorical variables are presented as percentages and analyzed using χ 2 tests. Descriptive statistics are used to describe demographic and clinical characteristics, with χ 2 and Kruskal-Wallis tests for analysis. Results This study recruited 7536 community participants (age > 50 years) from the multiethnic areas of western China. However, due to the failure of some community participants to complete relevant examinations and due to partial data loss, 4500 participants were finally included. Figure 1 shows the sarcopenia-screening process used for participants, which is based on the sarcopenia-screening process recommended by AWGS 2019 for medical institutions and clinical research. Among them, there were 3474 patients (77.2%) in the nonsarcopenia group, 408 (9.07%) in the diagnosed sarcopenia group, 618 (13.73%) in the severe sarcopenia group, and 1026 (22.8%) in the sarcopenia group. Figure 1 Study flowchart. Recruitment of individuals from the multiethnic regions in western China, following the diagnostic procedure of AWGS2019 for muscular dystrophy screening. GS, gait speed; HS, handgrip strength; MM, muscle mass. Table 1 shows the demographic and clinical characteristics of participants in the multiethnic areas in the western region. There were significant differences among the various groups of patients with muscular dystrophy in terms of ethnicity, age, education level, grip strength, ASMI, chronic diseases, and depression. There were no significant differences in terms of gender, smoking, drinking, marital status, sleep quality, living alone, gait speed, and household labor. Table 1 General demographic and clinical characteristics of individuals with different muscular dystrophies (n = 4500). Characteristics Nonsarcopenia Diagnosed sarcopenia Severe sarcopenia P-value (n = 3474) (n = 408) (n = 618) Age (years) 62.28 ± 8.223 62.17 ± 8.421 62.98 ± 8.302 0.135 Stratification by age stratification 0.031 50 ≤ Age < 65 1544 (44.4%) 178 (44.6%) 1544 (40.5%) 65 ≤ Age < 74 1346 (38.7%) 152 (37.7%) 242 (39.2%) 75 ≤ Age < 84 504 (14.5%) 65 (15.9%) 115 (16.8%) 85 ≤ Age 80 (2.3%) 13 (3.2%) 11 (1.8%) Sex 0.406 Male 1272 (36.6%) 146 (35.8%) 209 (33.8%) Female 2202 (63.4%) 262 (64.2%) 409 (66.2%) Smoking history 0.301 Yes 560 (17.1%) 79 (20.2%) 99 (16.9%) No 2717 (82.9%) 313 (79.8%) 488 (83.1%) Drinking alcohol 0.169 Yes 846 (25.8%) 94 (24.0%) 131 (22.3) No 2431 (74.2%) 298 (76.0%) 456 (77.7%) Ethnicity < 0.001 Han 1435 (41.3%) 179 (43.9%) 323 (52.3%) Zang 950 (27.3%) 134 (32.8%) 149 (24.1%) Qiang 892 (25.6%) 66 (16.2%) 92 (14.9%) Yi 136 (3.9%) 27 (6.6%) 50 (8.1%) Others 61 (1.9%) 2 (0.5%) 4 (0.6%) Education level < 0.01 Primary school or below 2255 (64.9%) 278 (68.1%) 434 (70.2%) Middle or senior school 1073 (30.9%) 116 (28.4%) 163 (26.4%) University level or above 146 (4.2%) 14 (3.4%) 21 (3.4%) Marital status 0.222 Singlehood 26 (0.7%) 4 (1.0%) 14 (0.2%) Married 2768 (79.7%) 334 (81.9%) 491 (79.4%) Divorced 56 (1.6%) 1 (0.2%) 10 (1.6%) Widowed 443 (12.8%) 54 (13.2%) 87 (14.1%) Vague 181 (5.2%) 15 (3.7%) 29 (4.7%) Sleep quality 0.083 PQSI > 5 1537 (46.8%) 206 (52.6%) 272 (46.2%) PQSI ≤ 5 1748 (53.2%) 186 (47.4%) 317 (53.8%) Living alone 0.564 Yes 161 (4.6%) 16 (3.9%) 33 (5.3%) No 3313 (95.4%) 392 (96.1) 585 (94.7%) Grip strength mean(± SD) 22.15 (8.75) 21.90 (8.80) 20.97 (8.65) < 0.001 Gait speed mean(± SD) 0.853 (0.265) 0.867 (0.295) 0.8311 (0.278) 0.078 ASMI mean(± SD) 6.639 (0.943) 6.651 (0.971) 6.515 (0.865) 0.009 Chronic diseases 0.034 Yes 1456 (44.3%) 174 (44.4%) 291 (49.4%) No 1829 (55.7%) 218 (55.6%) 298 (50.6%) Indoor housework 0.588 Yes 978 (30%) 125 (32%) 184 (31.5%) No 2284 (70%) 266 (9%) 400 (68.5%) Outdoor housework 0.114 Yes 1928 (59.3%) 237 (60.6%) 320 (55.0%) No 1324 (40.7%) 154 (39.4%) 262 (45.0%) Depressive status 0.011 GDS-15, < 5 2665 (76.7%) 334 (81.9%) 456 (73.8%) GDS-15, ≥ 5 809 (23.3%) 74 (18.1%) 162 (26.2%) Anxiety status 0.543 GAD-7 < 5 2836 (81.6%) 326 (79.8%) 496 (80.3%) GAD-7 ≥ 5 638 (18.4%) 82 (20.1%) 122 (19.7%) Other ethnicities including Zhuang, Mongolian, Uygur, Bai, Dong, Manchu, Hui, and Tujia. Data are presented as mean ± standard deviation. Table 2 presents the potential risk factors associated with different groups of muscular dystrophy. Specifically, the Han population had a significant association with severe muscular dystrophy (OR: 3.433, 95%CI 1.239–9.508), whereas the Tibetan population showed a correlation with diagnosed muscular dystrophy (OR: 4.302, 95%CI 1.040–17.798), the Yi population with diagnosed muscular dystrophy (OR: 6.055, 95%CI 1.359–26.276), and severe muscular dystrophy (OR: 5.607, 95%CI 1.938–16.22). Additionally, individuals who did not receive formal education were found to be associated with severe muscular dystrophy (OR: 1.417, 95%CI 1.085–1.851), those with primary education or below with diagnosed muscular dystrophy (OR: 1.514, 95%CI 1.116–2.054), and severe muscular dystrophy (OR: 1.467, 95%CI 1.129–1.908). Lastly, the presence of chronic diseases was also associated with diagnosed muscular dystrophy (OR: 1.227, 95%CI 1.029–1.462). Table 2 Multiple regression analysis of diverse risk factors associated with different muscular dystrophy groups in the western region of the multiethnic areas of China (n = 4500). Factors Diagnosed sarcopenia (n = 408) Severe sarcopenia (n = 618) β OR 95%CI P-value β OR 95%CI P-value Age (years) a –0.02 0.98 0.986–1.01 0.797 0.01 1.01 1.000–1.021 0.053 Sex Male 0.036 1.037 0.837–1.284 0.742 0.123 1.13 0.944–1.354 0.183 Female b 1 1 Smoking history No –2.03 0.817 0.628–1.062 0.131 0.016 1.016 0.804–1.284 0.804 Yes b 1 1 Drinking alcohol No 0.098 1.103 0.864–1.409 0.431 0.192 1.211 0.982–1.494 0.982 Yes b 1 1 Ethnicity Han 1.336 3.805 0.922–15.69 0.065 1.233 3.433 1.239–9.508 0.018 Zang 1.459 4.302 1.040–17.79 0.044 0.872 2.392 0.857–6.675 0.096 Qiang 0.814 2.257 0.540–9.435 0.265 0.453 1.574 0.559–4.424 0.391 Yi 1.801 6.055 1.359–26.27 0.016 1.724 5.607 1.938–16.22 0.001 Others b 1 1 Education level No formal education 0.117 1.124 0.813–1.553 0.479 0.394 1.417 1.085–1.851 0.01 Primary school or below 0.415 1.514 1.116–2.054 0.008 0.383 1.467 1.129–1.908 0.004 Middle or senior school 0.15 1.162 0.824–1.639 0.391 0.217 1.242 0.929–1.661 0.144 University level or above b 1 1 Living alone No 0.149 1.161 0.790–1.705 0.447 0.324 1.382 0.750–2.545 0.299 Yes b 1 1 Chronic diseases No 0.204 1.227 1.029–1.462 0.023 0.202 1.223 0.947–1.581 0.123 Yes b 1 1 Indoor housework No –0.072 0.931 0.770–1.126 0.46 0.021 1.022 0.773–1.345 0.879 Yes b 1 1 Outdoor housework No 0.176 1.192 0.998–1.424 0.052 0.231 1.26 0.971–1.635 0.082 Yes b 1 1 CI, confidence interval; OR, odds ratio. a Nonsarcopenia group is the reference. b The variable is the reference. Multivariate logistic regression analysis of sleep quality and sarcopenia groups is presented in Table 3 . As a result of adjusting for potential confounders such as age, gender, education level, smoking, and chronic diseases, the sleep quality (OR: 0.717, 95%CI 0.550–0.934) was found to be significantly associated with severe sarcopenia but not with diagnosed sarcopenia. Table 3 Association of sleep quality a on different sarcopenia groups in the multiethnic population of western China (n = 4500). Unadjusted model Adjusted c model β OR 95%CI P-value β OR 95%CI P-value Diagnosed sarcopenia b –0.024 0.976 0.818–1.163 0.785 –0.117 0.89 0.740–1.069 0.212 Severe sarcopenia b –0.255 0.775 0.600–1.001 0.051 –0.333 0.717 0.550–0.934 0.014 CI, confidence interval; OR odds ratio. a PQSI ≥ 5 is the reference. b Nonsarcopenia is the reference. c Adjusted for age, sex, smoking history, drinking alcohol, ethnicity, education level, living alone, chronic diseases, indoor housework, and outdoor housework by logistic regression. Using multiple logistic regression analysis and adjusting for confounding factors, no significant relationship between anxiety and different types of sarcopenia was noted (Table 4 ). A multivariable logistic regression analysis table for depression and sarcopenia is shown in Table 5 . A significant association between depression and diagnosed sarcopenia (OR: 1.289, 95%CI 1.032–1.608) and severe sarcopenia (OR: 1.622, 95%CI 1.146–2.294) was found after adjusting for potential confounding factors. A multivariate logistic regression analysis of sleep, anxiety, depression, and different sarcopenia groups is shown in Table 6 . Depression (OR = 1.258, 95%CI 1.005–1.575) is significantly associated with diagnosed sarcopenia compared with the nonsarcopenia group. Sleep (OR = 0.738, 95%CI 0.568–0.959) and depression (OR = 1.845, 95%CI 1.291–2.638) are also significantly associated with severe sarcopenia. However, anxiety has no significant impact on different sarcopenia groups. Table 4 Association of anxiety a and different sarcopenia groups in the multiethnic population of western China (n = 4500). Unadjusted model Adjusted c model β OR 95%CI P-value β OR 95%CI P-value Diagnosed sarcopenia b 0.89 1.093 0.881–1.357 0.418 0.81 1.084 0.865–1.359 0.481 Severe sarcopenia b –0.022 0.978 0.715–1.337 0.889 –0.45 0.965 0.692–1.320 0.956 CI, confidence interval; OR odds ratio. a GAD-7 ≥ 5 is the reference. b Nonsarcopenia is the reference. c Adjusted for age, sex, smoking history, drinking alcohol, ethnicity, education level, living alone, chronic diseases, indoor housework, and outdoor housework by logistic regression. Table 5 Association of depression a and different sarcopenia groups in the multiethnic population of western China (n = 4500). Unadjusted model Adjusted model β OR 95%CI P-value β OR 95%CI P-value Diagnosed sarcopenia a 0.157 1.17 0.962–1.423 0.115 0.254 1.289 1.032–1.608 0.025 Severe sarcopenia a 0.472 1.603 1.177–2.184 0.003 0.484 1.622 1.146–2.294 0.006 CI, confidence interval; OR odds ratio. a GDS-15 ≥ 5 is the reference. b Nonsarcopenia is the reference. c Adjusted for age, sex, smoking history, drinking alcohol, ethnicity, education level, living alone, chronic diseases, indoor housework, and outdoor housework by logistic regression. Table 6 Correlation of sleep quality, anxiety, and depression with different sarcopenia groups in the multiethnic population of western China (n = 4500). β OR 95%CI P-value Diagnosed sarcopenia a Constant 1.549 0.00 Sleep quality –0.055 0.947 0.791–1.133 0.55 Anxiety status 0.021 1.021 0.810–1.287 0.86 Depressive status 0.23 1.258 1.005–1.575 0.045 Severe sarcopenia a Constant –0.655 0 Sleep quality –0.303 0.738 0.568–0.959 0.023 Anxiety status –0.123 0.884 0.633–1.236 0.472 Depressive status 0.613 1.845 1.291–2.638 0.001 CI, confidence interval; OR odds ratio. a Nonsarcopenia was the reference. Discussion This study is based on the multiethnic population in western China using the diagnostic and classification methods recommended by the 2019 AWGS for sarcopenia. The screening process for sarcopenia used for clinical research was selected, and the following 3 groups were made: nonsarcopenia group, diagnosed sarcopenia group, and severe sarcopenia group. Research results from some European and American countries suggest that the prevalence of sarcopenia varies considerably depending on the actual methods used in various studies and the chosen cutoff values. This variation is influenced by how muscle mass is assessed using different diagnostic instruments as well as by factors including ethnicity, place of residence, and age [ 22 , 23 ]. There was a 22.8% prevalence of sarcopenia in the current study; however, Taiwanese researchers screened elderly individuals > 65 years of age for sarcopenia using the diagnostic criteria set by the European Working Group on Sarcopenia in Older People and reported a prevalence of 14.4% [ 23 ]. The prevalence of muscular dystrophy is higher in Taiwan than in other regions. Apart from differences in diagnostic criteria, this outcome may be related to the multiethnic population and lower standards of living in the western regions. Although our study included subjects over a wide age range, the incidence of muscular dystrophy was found to vary among different ethnic groups and to correlate differently with different types of muscular dystrophy. These differences among different ethnic groups may be related to genetics, dietary habits, and even religious beliefs, which require further in-depth research. Sleep quality and sarcopenia Sleep quality is an important aspect required to maintain physical and mental health. Disruptions or changes in circadian rhythms are associated with the development of many chronic diseases including aging and sarcopenia. Sleep quality is a multidimensional structure that includes sleep latency, awakening after sleep onset, frequency and number of awakenings, and subjective reports of feelings and mental state upon waking. Furthermore, among older adults, it is associated with a poor quality of life, increased incidence of other diseases, and higher mortality rates [ 24 , 25 ]. Studies have reported that the sleep–wake cycle is related to the maintenance of skeletal muscles. It plays a crucial role in numerous physiological functions, muscle structure, and metabolism of skeletal muscles. In models of disrupted circadian rhythm, the absence of the Bma/1 gene has been reported to lead to sarcopenia and other pathological diseases of the muscle, including reduced mitochondrial density and altered mitochondrial respiration, fiber type displacement, and impaired structure of muscle segments [ 26 , 27 ]. The circadian clock–suppressing gene Rev - erbα , which affects sleep, also plays a crucial role in regulating skeletal muscle metabolism [ 28 , 29 ]. Sleep initiation and/or maintenance in elderly Japanese individuals is associated with sarcopenia [ 30 ]. This study found a significant correlation between sleep quality (OR: 0.717, 95%CI 0.550–0.934) and severe sarcopenia but no significant correlation with diagnosed sarcopenia. In the aging population with sleep disorders, improving sleep quality may slow down muscle loss and prevent or delay the onset of severe sarcopenia. Relationship between anxiety, depression, and sarcopenia Comorbidity between anxiety and depression is very common in the elderly population; it is also common with several other physical illnesses and associated with cognitive decline [ 31 ]. Anxiety and depression are also related to circadian rhythms, which are closely related to skeletal muscle function [ 26 ]. Anxiety and depression are closely associated with fragmentation of the 24-hour activity rhythm in middleaged and elderly individuals [32]. Anxiety, depression, and physical activity are significantly correlated, with lower levels of daily activity being a core feature of mood disorders [ 33 ]. Currently, there is limited research on anxiety disorders; however, some studies indicate that their effects are similar to those of depression in certain aspects, such as psychomotor retardation, lower levels of daily activities, and circadian rhythm disturbances [ 34 ]. In this study, anxiety in the multiethnic region of western China was not significantly associated with different muscular dystrophy groups; however, depression showed significant associations with the diagnosed muscular dystrophy group and severe muscular dystrophy group. Depression is characterized by low mood, slow thinking, disrupted sleep or appetite, and feelings of fatigue, and is a common mental disorder in the elderly. Some studies have reported a relationship between depression and body composition that involves factors such as skeletal muscle mass, strength, and muscle function, all of which are directly related to sarcopenia [ 35 , 36 , 37 ]. Both sarcopenia and depression are associated with decreased physical activity, upregulation of inflammatory factors, and hormonal dysregulation of the hypothalamic–pituitary–adrenal axis [ 38 ]. Some studies have reported that there is no significant association between myasthenia gravis and depression [ 39 ], whereas there are reports suggesting a significant association between the two [ 40 ]. Based on the 2019 AWGS diagnostic criteria for sarcopenia, we subdivided sarcopenia into groups and collected data on depression, which further confirmed a significant correlation between depression and both the diagnosed sarcopenia and severe sarcopenia groups. Our study has some limitations. Although the PSQI is a validated and reliable measurement method, it cannot perfectly capture sleep parameters as effectively as polysomnography, which is the gold standard. Additionally, for subjects with anxiety and depression, further analyses are needed to determine if their medication status may have intervened with muscle function. Conclusions In the multiethnic population in western China, a significant correlation between sleep and severe muscle wasting syndrome, depression, and diagnosed muscle wasting syndrome and severe muscle wasting syndrome was found, but no significant correlation between anxiety and muscle wasting syndrome was noted after stratification. Preventing depression or intervention for depression in this population may be an approach to delay or reduce the occurrence of muscle wasting syndrome and may also help formulate specific medical policies. Further longitudinal studies are needed to confirm the relationship between sleep, anxiety, depression, and muscle wasting syndrome. Abbreviations CI confidence interval GAD-7 Generalized anxiety disorder GDS-15 15-item Geriatric Depression Scale OR odds ratio PSQI Pittsburgh Sleep Quality Index WCHAT West China Health and Aging Trend. Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of West China Hospital, Sichuan University (reference: 2017 − 445). This study was registered at the China Clinical Trial Center (Registration Number: ChiCTR1800018895). All participants signed the informed consent before participating. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interest. Funding Supported by the National Key R&D Program of China (2018YFC2000305, 2020YFC2005600, 2020YFC2005602 and 2020YFC0840101); 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (ZYGD20010 and ZY2017201); Geriatric Health Care and Medical Research Center, Sichuan University, Chengdu, Sichuan Province, China; Sichuan Science and Technology Program (No. 2023NSFSC1158); Project funded by China Postdoctoral Science Foundation (No.2023M732473); National Clinical Research Center for Geriatrics, West China Hospital (No. Z2024JC006）. Chengdu Science and Technology Bureau Major Science and Technology Application Demonstration Project (2019YF0900083SN); The financial sponsors had no role in the design, implementation, analyses, or reporting of the results. Authors' contributions BRD designed research; ZGX, XLL, NH, GCZ, XLJ, XX, FJH, MLG conducted research; ZGX analyzed data; and ZGX and XLL wrote the paper. ZGX had primary responsibility for final content. All authors read and approved the final manuscript. Acknowledgements We would like to thank all study participants and their families for their cooperation in the research team. References Fang EF, Scheibye-Knudsen M, Jahn HJ, et al. A research agenda for aging in China in the 21st century. Ageing Res Rev. 2015;24(Pt B):197-205. Marck A, Antero J, Berthelot G, et al. Age-Related Upper Limits in Physical Performances. J Gerontol A Biol Sci Med Sci. 2019;74(5):591-599. Kok RM, Reynolds CF 3rd. Management of Depression in Older Adults: A Review. JAMA. 2017;317(20):2114-2122. Karlsson B, Johnell K, Sigström R, et al. Depression and Depression Treatment in a Population-Based Study of Individuals Over 60 Years Old Without Dementia. Am J Geriatr Psychiatry. 2016;24(8):615-623. Malinowska KB, Ikezoe T, Ichihashi N, et al. Self-reported quality of sleep is associated with physical strength among community-dwelling young-old adults. Geriatr Gerontol Int. 2017;17(11):1808-1813. Hofmann M, Halper B, Oesen S, et al. Serum concentrations of insulin-like growth factor-1, members of the TGF-beta superfamily and follistatin do not reflect different stages of dynapenia and sarcopenia in elderly women. Exp Gerontol. 2015;64:35-45. Kris-Etherton PM, Petersen KS, Hibbeln JR, Hurley D, Kolick V, Peoples S, Rodriguez N, Woodward-Lopez G. Nutrition and behavioral health disorders: depression and anxiety. Nutr Rev. 2021;79(3):247-260. Cawthon PM, Manini T, Patel SM, et al. Putative Cut-Points in Sarcopenia Components and Incident Adverse Health Outcomes: An SDOC Analysis. J Am Geriatr Soc. 2020;68(7):1429-1437. Xu J, Wan CS, Ktoris K, et al. Sarcopenia Is Associated with Mortality in Adults: A Systematic Review and Meta-Analysis. Gerontology. 2022;68(4):361-376. Ensrud KE, Blackwell TL, Ancoli-Israel S, et al. Sleep disturbances and risk of frailty and mortality in older men. Sleep Med. 2012;13(10):1217-25. Goldman SE, Stone KL, Ancoli-Israel S, et al. Poor sleep is associated with poorer physical performance and greater functional limitations in older women. Sleep. 2007;30(10):1317-24. Dam TT, Ewing S, Ancoli-Israel S, et al. Association between sleep and physical function in older men: the osteoporotic fractures in men sleep study. J Am Geriatr Soc. 2008;56(9):1665-73. Denison HJ, Jameson KA, Sayer AA, et al. Poor sleep quality and physical performance in older adults. Sleep Health. 2021;7(2):205-211. Wolitzky-Taylor KB, Castriotta N, Lenze EJ, et al. Anxiety disorders in older adults: a comprehensive review. Depress Anxiety. 2010;27(2):190-211. Liu X, Hou L, Xia X, et al. Prevalence of sarcopenia in multi ethnics adults and the association with cognitive impairment: findings from West-China health and aging trend study. BMC Geriatr. 2020;20(1):63. Chen LK, Woo J, Assantachai P, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc. 2020;21(3):300-307.e2. Hou L, Liu X, Zhang Y, et al. Cohort Profile: West China Health and Aging Trend (WCHAT). J Nutr Health Aging. 2021;25(3):302-310. Wang H, Hai S, Cao L, et al. Estimation of prevalence of sarcopenia by using a new bioelectrical impedance analysis in Chinese community-dwelling elderly people. BMC Geriatr. 2016;16(1):216. Tosato M, Marzetti E, Cesari M, et al. Measurement of muscle mass in sarcopenia: from imaging to biochemical markers. Aging Clin Exp Res. 2017;29(1):19-27. Toussaint A, Hüsing P, Gumz A, et al. Sensitivity to change and minimal clinically important difference of the 7-item Generalized Anxiety Disorder Questionnaire (GAD-7). J Affect Disord. 2020;265:395-401. Shin C, Park MH, Lee SH, et al. Usefulness of the 15-item geriatric depression scale (GDS-15) for classifying minor and major depressive disorders among community-dwelling elders. J Affect Disord. 2019;259:370-375. von Haehling S, Morley JE, Anker SD. An overview of sarcopenia: facts and numbers on prevalence and clinical impact. J Cachexia Sarcopenia Muscle. 2010;1(2):129-133. Lin CC, Lin WY, Meng NH, et al. Sarcopenia prevalence and associated factors in an elderly Taiwanese metropolitan population. J Am Geriatr Soc. 2013;61(3):459-62. Cole CS, Richards KC, Beck CC, et al. Relationships among disordered sleep and cognitive and functional status in nursing home residents. Res Gerontol Nurs. 2009;2(3):183-91. Morgan K, Hartescu I. Sleep duration and all-cause mortality: links to physical activity and prefrailty in a 27-year follow up of older adults in the UK. Sleep Med. 2019;54:231-237. Vitale JA, Bonato M, La Torre A, Banfi G. The Role of the Molecular Clock in Promoting Skeletal Muscle Growth and Protecting against Sarcopenia. Int J Mol Sci. 2019;20(17):4318. Lipton JO, Yuan ED, Boyle LM, et al. The Circadian Protein BMAL1 Regulates Translation in Response to S6K1-Mediated Phosphorylation. Cell. 2015;161(5):1138-1151. Delezie J, Dumont S, Dardente H, et al. The nuclear receptor REV-ERBα is required for the daily balance of carbohydrate and lipid metabolism. FASEB J. 2012;26(8):3321-35. Yin L, Wu N, Lazar MA. Nuclear receptor Rev-erbalpha: a heme receptor that coordinates circadian rhythm and metabolism. Nucl Recept Signal. 2010;8:e001. Shibuki T, Iida M, Harada S, et al. The association between sleep parameters and sarcopenia in Japanese community-dwelling older adults. Arch Gerontol Geriatr. 2023;109:104948. Wolitzky-Taylor KB, Castriotta N, Lenze EJ, et al. Anxiety disorders in older adults: a comprehensive review. Depress Anxiety. 2010;27(2):190-211. Luik AI, Zuurbier LA, Direk N, et al. 24-Hour activity rhythm and sleep disturbances in depression and anxiety: A population-based study of middle-aged and older presons. Depress Anxiety. 2015;32(9):684-92. Dittoni S, Mazza M, Losurdo A, et al. Psychological functioning measures in patients with primary insomnia and sleep state misperception. Acta Neurol Scand. 2013;128(1):54-60. Difrancesco S, Lamers F, Riese H, et al. Sleep, circadian rhythm, and physical activity patterns in depressive and anxiety disorders: A 2-week ambulatory assessment study. Depress Anxiety. 2019;36(10):975-986. Gariballa S, Alessa A. Associations between low muscle mass, blood-borne nutritional status and mental health in older patients. BMC Nutr. 2020;6:6. Yuenyongchaiwat K, Buranapuntalug S, Pongpanit K, et al. Anxiety and Depression Symptomatology Related to Inspiratory Muscle Strength and Functional Capacity in Preoperative Cardiac Surgery Patients: A Preliminary Cross-sectional Study. Indian J Psychol Med. 2020 Jul;42(6):549-554. Moon JH, Kong MH, Kim HJ. Low Muscle Mass and Depressed Mood in Korean Adolescents: a Cross-Sectional Analysis of the Fourth and Fifth Korea National Health and Nutrition Examination Surveys. J Korean Med Sci. 2018;33(50):e320. Hallgren M, Herring MP, Owen N, et al. Exercise, Physical Activity, and Sedentary Behavior in the Treatment of Depression: Broadening the Scientific Perspectives and Clinical Opportunities. Front Psychiatry. 2016;7:36. Patino-Hernandez D, David-Pardo DG, Borda MG, et al. Association of Fatigue With Sarcopenia and its Elements: A Secondary Analysis of SABE-Bogotá. Gerontol Geriatr Med. 2017;3:2333721417703734. Chang KV, Hsu TH, Wu WT, et al. Is sarcopenia associated with depression? A systematic review and meta-analysis of observational studies. Age Ageing. 2017;46(5):738-746. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4370867","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":307892219,"identity":"82576044-e276-48fd-aef7-172c1745cdae","order_by":0,"name":"Zhigang Xu","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Zhigang","middleName":"","lastName":"Xu","suffix":""},{"id":307892220,"identity":"fadd7114-7dac-4f6b-8e40-19f2ab6803df","order_by":1,"name":"Xiaolei Liu","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Xiaolei","middleName":"","lastName":"Liu","suffix":""},{"id":307892221,"identity":"f6889b15-886d-44ed-8567-a5ec30642ad8","order_by":2,"name":"Huang Ning","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Huang","middleName":"","lastName":"Ning","suffix":""},{"id":307892222,"identity":"ba393173-34d7-45c3-becf-cc189d6cd42b","order_by":3,"name":"Gongchang Zhang","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Gongchang","middleName":"","lastName":"Zhang","suffix":""},{"id":307892223,"identity":"7d5afce2-81eb-45c0-b4a3-a8764c73d3ef","order_by":4,"name":"Shuli Jia","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Shuli","middleName":"","lastName":"Jia","suffix":""},{"id":307892224,"identity":"317ab175-f5e2-4141-b85d-c3512eb2a3ca","order_by":5,"name":"Xin Xia","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Xia","suffix":""},{"id":307892225,"identity":"215b7556-8a4a-4ba1-bd68-595c38119a8f","order_by":6,"name":"Fengjuan Hu","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Fengjuan","middleName":"","lastName":"Hu","suffix":""},{"id":307892226,"identity":"5c9c8886-5184-48c7-a73d-4226fa14b313","order_by":7,"name":"Meiling Ge","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Meiling","middleName":"","lastName":"Ge","suffix":""},{"id":307892227,"identity":"44f3afa0-bbc2-4cf8-bd45-77e8bba342bd","order_by":8,"name":"Birong Dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBACA2YwBSIZGx9+qADS7EBmA3FamJuNJc4wMPAwE9LCANfC3ibB20aEFnN23oePC35Zy5nzL2yQkJxnk7ifmfngwxkMdnK6OPRZNrMbG8/sSze2nPGwwaBwW1piDzNbsuEGhmRjswM4HHaYjU2at+dw4oYbBxsSJLcdBmrhMZN8wHAgcRsxWg7wziFWC88PoJbzjY0NvA1QLRvwaLFsZmM25m1INza4wdjMLHEszbjnMNAvMwxw+8Wc/xjjY54/1nIG548///mhxka2vb354MOeCjs5XFrAgBEYHQwSCSgOxqMcDP4AMT8+Q0fBKBgFo2BEAwCFg1zcFvsdNAAAAABJRU5ErkJggg==","orcid":"","institution":"Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Birong","middleName":"","lastName":"Dong","suffix":""}],"badges":[],"createdAt":"2024-05-05 08:25:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4370867/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4370867/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57791218,"identity":"5794ebfb-08b4-4891-aa39-d375b1d08b6a","added_by":"auto","created_at":"2024-06-05 17:35:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":51122,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flowchart. Recruitment of individuals from the multiethnic regions in western China, following the diagnostic procedure of AWGS2019 for muscular dystrophy screening. GS, gait speed; HS, handgrip strength; MM, muscle mass.\u003c/p\u003e","description":"","filename":"Figure1.Studyflowchart.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4370867/v1/cb61648cfc61d412594cef86.jpg"},{"id":63568504,"identity":"e8552338-bf58-45c5-b959-42270ccd2134","added_by":"auto","created_at":"2024-08-29 16:50:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1034864,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4370867/v1/2096666b-e40c-4153-87b8-8b09dcd9911d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlation between sleep disorder, anxiety, depression, and sarcopenia in multiethnic areas of western China","fulltext":[{"header":"Background","content":"\u003cp\u003eThe aging population of China is on the rise. By the year 2050, the number of individuals in China over 65 years of age is projected to reach 400\u0026nbsp;million, including 150\u0026nbsp;million people over 80 years of age, along with an increase in the public medical and health burden [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Therefore, age-related diseases have attracted increasing attention from society. With an increase in age, the functions of the human body gradually decline when individuals reach the middle-age stage [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Certain groups of people, especially the elderly, may experience an increased incidence of sleep disorders, anxiety, and even depression [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These psychological and mental changes may also bring about changes in hormone levels and directly affect protein synthesis in the body. Some of these proteins may be key in maintaining muscle mass [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. At the same time, the occurrence of sleep, anxiety, and even depression may alter daily life and diet [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and cause changes in muscle metabolism.\u003c/p\u003e \u003cp\u003eAs a progressive and systemic disease of the skeletal muscle, sarcopenia occurs with aging and is associated with a high rate of adverse outcomes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Hospitalization rates and adverse outcomes, including death, are associated with sarcopenia. Older adults with severe muscle loss have an increased risk of shortterm mortality, making sarcopenia one of the predictors of mortality in community-dwelling older adults [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This also indicates that early intervention in the prevention and treatment of sarcopenia is particularly important for healthcare services in China.\u003c/p\u003e \u003cp\u003ePoor sleep quality is associated with impaired physical function, mortality, frailty, and falls in older adults [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Sleep may impact muscle mass and strength through metabolism, hormones, and immune factors, which, in turn, may affect physical performance [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Anxiety is common in the elderly population; it is often a comorbidity with depression and is associated with cognitive decline [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Cognitive function is related to sarcopenia [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In ethnically diverse regions such as China, there is limited research on the association between sleep, anxiety, depression, and sarcopenia.\u003c/p\u003e \u003cp\u003eDue to differences such as ethnicity, lifestyle, dietary habits, economy, geography, and beliefs between Western countries, such as Europe, and Asian countries, Europe and Asia have each established different working groups on sarcopenia. These groups have formulated assessment methods and diagnostic criteria for sarcopenia, each having its own variations. Consequently, there are certain disparities in the reported prevalence of regional sarcopenia among different countries. China is a populous country in Asia having a multiethnic population. It could therefore be of great significance to study the situation of sarcopenia in the multiethnic regions of western China. Sarcopenia screening in medical institutions or clinical research, as recommended by the Asian Working Group for Sarcopenia, can further be separated into diagnosed sarcopenia and severe sarcopenia [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Most studies only present the correlation between single sleep or depression factors and overall sarcopenia. Thus, there is a lack of research on the relationship between anxiety and sleep, depression, and different sarcopenia subgroups, as well as a dearth of definitive research conclusions.\u003c/p\u003e \u003cp\u003eIn this study, we used the Western China Health and Aging Trends (WCHAT) longitudinal multicenter cohort study [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The aim of this study was to understand the prevalence and risk factors of sleep disorders, anxiety, depression, and sarcopenia in the multiethnic population of western China and to analyze the relationship between sleep, anxiety, and depression and different sarcopenia subgroups.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThe study population comprised individuals from multiple provinces and cities in the western region of China, representing various ethnic groups. This study is based on the ongoing prospective cohort study WCHAT and the research team has published a detailed description of the methodology and study design [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The Ethics Committee of West China Hospital, Sichuan University, China, reviewed and approved this study (reference number: 2017\u0026thinsp;\u0026minus;\u0026thinsp;445), and all participants signed the informed consent form [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The subjects included in this study were from 4 provinces in western China, including Sichuan, Xinjiang, Guizhou, and Yunnan, with the majority from Sichuan. The participants represent various ethnic groups such as Han, Tibetan, Qiang, Yi, Hui, Zhuang, and Miao.\u003c/p\u003e \u003cp\u003eInclusion criteria for participants: living in the region for \u0026ge;\u0026thinsp;3 years; age\u0026thinsp;\u0026ge;\u0026thinsp;50 years; voluntary participation in the study. Exclusion criteria: expected lifespan\u0026thinsp;\u0026lt;\u0026thinsp;6 months; acute diseases of important organs such as the heart, liver, kidneys, etc., and severe diseases such as respiratory failure; refusal to participate in the survey.\u003c/p\u003e \u003cp\u003eEventually, 7536 participants from the multiethnic regions in western China were recruited, and data on bioelectrical impedance analysis (BIA) were obtained from 4500 individuals. Information on sleep, anxiety, and depression scales was ultimately used for the analysis of individuals with muscle dystrophy. All personnel involved with data collection in this study received rigorous training, and health checks were conducted by relevant professional technicians.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eScreening for sarcopenia\u003c/h2\u003e \u003cp\u003eSarcopenia is characterized by the accelerated loss of muscle mass and function. For primary healthcare or community-based health purposes, the Asian Working Group on Sarcopenia (AWGS) has defined \u0026ldquo;possible sarcopenia\u0026rdquo; as low muscle strength or physical function [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In this study, the screening methods recommended by AWGS were followed, using the sarcopenia assessment pathways corresponding to clinical research, categorizing sarcopenia into diagnosed sarcopenia and severe sarcopenia. The specific assessment methods for obtaining relevant data are as stated below.\u003c/p\u003e \u003cp\u003eIn this study, we chose BIA for assessment of the muscle mass of the study population, and the InBody 770 body composition instrument was used for data collection. The reliability of this instrument was validated among the relevant population in China [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In accordance with the AWGS2019 recommendations, we used a cutoff value of 7.0 kg/m\u003csup\u003e2\u003c/sup\u003e for men and 5.7 kg/m\u003csup\u003e2\u003c/sup\u003e for women for skeletal muscle mass index (ASMI) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHand grip testing is used to reflect muscle strength. A dynamometer (EH101; Camry, Zhongshan) was used to measure the grip strength of the dominant hand of subjects. During the measurement, subjects were asked to stand with their feet naturally apart and arms hanging down, and to perform the maximum grip strength test on 2 separate occasions. The maximum value was recorded. The standard for weak grip strength is \u0026lt;\u0026thinsp;18 kg for females and \u0026lt;\u0026thinsp;28 kg for males [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe general gait speed test to measure walking speed requires participants to wear flat shoes and allows for the use of a walking aid. Participants can rest during the measurement but should not sit down. The AWGS recommends a critical value of 1 m/s for gait speed in individuals with muscle weakness [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSleep quality assessment\u003c/h2\u003e \u003cp\u003eThe Pittsburgh Sleep Quality Index (PSQI) is used to gauge sleep quality and serves as an indicator of subjective sleep quality for the past month. It consists of 19 items and is commonly used in the diagnosis of sleep disorders in both clinical and research settings, serving as a standardized assessment for patients with sleep difficulties [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. PSQI scores\u0026thinsp;\u0026ge;\u0026thinsp;5 indicate poor sleep quality, whereas scores\u0026thinsp;\u0026lt;\u0026thinsp;5 indicate good sleep quality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAnxiety assessment\u003c/h2\u003e \u003cp\u003eThe Generalized Anxiety Disorder Questionnaire (GAD-7) is used to measure anxiety. Currently, GAD-7 is one of the most widely used anxiety assessment measures in clinical practice and research because of its high diagnostic reliability and efficiency [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDepression assessment\u003c/h2\u003e \u003cp\u003eTo assess the symptoms of depression, the 15-item Geriatric Depression Scale was used, which was developed based on the unique symptoms such as somatic symptoms, anxiety, and cognitive decline commonly exhibited by older adults with depression. A score of \u0026ge;\u0026thinsp;15 or \u0026ge;\u0026thinsp;5 indicates depression [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGeneral information on the study population\u003c/h2\u003e \u003cp\u003eThis includes demographics (gender, age, ethnicity, marital status, employment status, living arrangement), lifestyle factors (alcohol consumption, smoking status), and health conditions (chronic diseases such as hypertension, diabetes, heart disease, chronic obstructive pulmonary disease).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe study population data were analyzed using SPSS 22.0. Continuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and \u003cem\u003et\u003c/em\u003e-test was used for group comparisons. Categorical variables are presented as percentages and analyzed using χ\u003csup\u003e2\u003c/sup\u003e tests. Descriptive statistics are used to describe demographic and clinical characteristics, with χ\u003csup\u003e2\u003c/sup\u003e and Kruskal-Wallis tests for analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThis study recruited 7536 community participants (age\u0026thinsp;\u0026gt;\u0026thinsp;50 years) from the multiethnic areas of western China. However, due to the failure of some community participants to complete relevant examinations and due to partial data loss, 4500 participants were finally included. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the sarcopenia-screening process used for participants, which is based on the sarcopenia-screening process recommended by AWGS 2019 for medical institutions and clinical research. Among them, there were 3474 patients (77.2%) in the nonsarcopenia group, 408 (9.07%) in the diagnosed sarcopenia group, 618 (13.73%) in the severe sarcopenia group, and 1026 (22.8%) in the sarcopenia group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Study flowchart. Recruitment of individuals from the multiethnic regions in western China, following the diagnostic procedure of AWGS2019 for muscular dystrophy screening. GS, gait speed; HS, handgrip strength; MM, muscle mass.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the demographic and clinical characteristics of participants in the multiethnic areas in the western region. There were significant differences among the various groups of patients with muscular dystrophy in terms of ethnicity, age, education level, grip strength, ASMI, chronic diseases, and depression. There were no significant differences in terms of gender, smoking, drinking, marital status, sleep quality, living alone, gait speed, and household labor.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral demographic and clinical characteristics of individuals with different muscular dystrophies (n\u0026thinsp;=\u0026thinsp;4500).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNonsarcopenia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiagnosed sarcopenia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eSevere sarcopenia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3474)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;408)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;618)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.28\u0026thinsp;\u0026plusmn;\u0026thinsp;8.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.17\u0026thinsp;\u0026plusmn;\u0026thinsp;8.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.98\u0026thinsp;\u0026plusmn;\u0026thinsp;8.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStratification by age stratification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026thinsp;\u0026le;\u0026thinsp;Age\u0026thinsp;\u0026lt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1544 (44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178 (44.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1544 (40.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026thinsp;\u0026le;\u0026thinsp;Age\u0026thinsp;\u0026lt;\u0026thinsp;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1346 (38.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152 (37.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e242 (39.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u0026thinsp;\u0026le;\u0026thinsp;Age\u0026thinsp;\u0026lt;\u0026thinsp;84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e504 (14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (15.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115 (16.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e85\u0026thinsp;\u0026le;\u0026thinsp;Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1272 (36.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146 (35.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e209 (33.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2202 (63.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e262 (64.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e409 (66.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e560 (17.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (20.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99 (16.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2717 (82.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e313 (79.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e488 (83.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking alcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e846 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94 (24.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131 (22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2431 (74.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e298 (76.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e456 (77.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1435 (41.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e179 (43.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e323 (52.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e950 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134 (32.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149 (24.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQiang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e892 (25.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (16.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2255 (64.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e278 (68.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e434 (70.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle or senior school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1073 (30.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116 (28.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163 (26.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity level or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSinglehood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2768 (79.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e334 (81.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e491 (79.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e443 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (13.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87 (14.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVague\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e181 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePQSI\u0026thinsp;\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1537 (46.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e206 (52.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e272 (46.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePQSI\u0026thinsp;\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1748 (53.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e186 (47.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e317 (53.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3313 (95.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e392 (96.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e585 (94.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrip strength\u003c/p\u003e \u003cp\u003emean(\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.15 (8.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.90 (8.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.97 (8.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGait speed\u003c/p\u003e \u003cp\u003emean(\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.853 (0.265)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.867 (0.295)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8311 (0.278)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASMI\u003c/p\u003e \u003cp\u003emean(\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.639 (0.943)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.651 (0.971)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.515 (0.865)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1456 (44.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e174 (44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e291 (49.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1829 (55.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e218 (55.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e298 (50.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndoor housework\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e978 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e184 (31.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2284 (70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e266 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e400 (68.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutdoor housework\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1928 (59.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e237 (60.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e320 (55.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1324 (40.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154 (39.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e262 (45.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepressive status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDS-15, \u0026lt; 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2665 (76.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e334 (81.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e456 (73.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDS-15, \u0026ge; 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e809 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (18.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e162 (26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD-7\u0026thinsp;\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2836 (81.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e326 (79.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e496 (80.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD-7\u0026thinsp;\u0026ge;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e638 (18.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (20.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122 (19.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOther ethnicities including Zhuang, Mongolian, Uygur, Bai, Dong, Manchu, Hui, and Tujia. Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the potential risk factors associated with different groups of muscular dystrophy. Specifically, the Han population had a significant association with severe muscular dystrophy (OR: 3.433, 95%CI 1.239\u0026ndash;9.508), whereas the Tibetan population showed a correlation with diagnosed muscular dystrophy (OR: 4.302, 95%CI 1.040\u0026ndash;17.798), the Yi population with diagnosed muscular dystrophy (OR: 6.055, 95%CI 1.359\u0026ndash;26.276), and severe muscular dystrophy (OR: 5.607, 95%CI 1.938\u0026ndash;16.22). Additionally, individuals who did not receive formal education were found to be associated with severe muscular dystrophy (OR: 1.417, 95%CI 1.085\u0026ndash;1.851), those with primary education or below with diagnosed muscular dystrophy (OR: 1.514, 95%CI 1.116\u0026ndash;2.054), and severe muscular dystrophy (OR: 1.467, 95%CI 1.129\u0026ndash;1.908). Lastly, the presence of chronic diseases was also associated with diagnosed muscular dystrophy (OR: 1.227, 95%CI 1.029\u0026ndash;1.462).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple regression analysis of diverse risk factors associated with different muscular dystrophy groups in the western region of the multiethnic areas of China (n\u0026thinsp;=\u0026thinsp;4500).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eDiagnosed sarcopenia (n\u0026thinsp;=\u0026thinsp;408)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eSevere sarcopenia (n\u0026thinsp;=\u0026thinsp;618)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.986\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.000\u0026ndash;1.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.837\u0026ndash;1.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.944\u0026ndash;1.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.628\u0026ndash;1.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.804\u0026ndash;1.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking alcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.864\u0026ndash;1.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.982\u0026ndash;1.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.922\u0026ndash;15.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.239\u0026ndash;9.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.040\u0026ndash;17.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.857\u0026ndash;6.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQiang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.540\u0026ndash;9.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.559\u0026ndash;4.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.359\u0026ndash;26.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.938\u0026ndash;16.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.813\u0026ndash;1.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.085\u0026ndash;1.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school or\u003c/p\u003e \u003cp\u003ebelow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.116\u0026ndash;2.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.129\u0026ndash;1.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle or\u003c/p\u003e \u003cp\u003esenior school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.824\u0026ndash;1.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.929\u0026ndash;1.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity level or\u003c/p\u003e \u003cp\u003eabove\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.790\u0026ndash;1.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.750\u0026ndash;2.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.029\u0026ndash;1.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.947\u0026ndash;1.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndoor housework\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.770\u0026ndash;1.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.773\u0026ndash;1.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutdoor housework\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.998\u0026ndash;1.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.971\u0026ndash;1.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCI, confidence interval; OR, odds ratio. \u003csup\u003ea\u003c/sup\u003eNonsarcopenia group is the reference. \u003csup\u003eb\u003c/sup\u003eThe variable is the reference.\u003c/p\u003e \u003cp\u003eMultivariate logistic regression analysis of sleep quality and sarcopenia groups is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. As a result of adjusting for potential confounders such as age, gender, education level, smoking, and chronic diseases, the sleep quality (OR: 0.717, 95%CI 0.550\u0026ndash;0.934) was found to be significantly associated with severe sarcopenia but not with diagnosed sarcopenia.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of sleep quality\u003csup\u003ea\u003c/sup\u003e on different sarcopenia groups in the multiethnic population of western China (n\u0026thinsp;=\u0026thinsp;4500).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eUnadjusted model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eAdjusted\u003csup\u003ec\u003c/sup\u003e model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnosed sarcopenia\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.818\u0026ndash;1.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.740\u0026ndash;1.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere sarcopenia\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.600\u0026ndash;1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.550\u0026ndash;0.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCI, confidence interval; OR odds ratio. \u003csup\u003ea\u003c/sup\u003ePQSI \u0026ge; 5 is the reference. \u003csup\u003eb\u003c/sup\u003eNonsarcopenia is the reference. \u003csup\u003ec\u003c/sup\u003eAdjusted for age, sex, smoking history, drinking alcohol, ethnicity, education level, living alone, chronic diseases, indoor housework, and outdoor housework by logistic regression.\u003c/p\u003e \u003cp\u003eUsing multiple logistic regression analysis and adjusting for confounding factors, no significant relationship between anxiety and different types of sarcopenia was noted (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A multivariable logistic regression analysis table for depression and sarcopenia is shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. A significant association between depression and diagnosed sarcopenia (OR: 1.289, 95%CI 1.032\u0026ndash;1.608) and severe sarcopenia (OR: 1.622, 95%CI 1.146\u0026ndash;2.294) was found after adjusting for potential confounding factors. A multivariate logistic regression analysis of sleep, anxiety, depression, and different sarcopenia groups is shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Depression (OR\u0026thinsp;=\u0026thinsp;1.258, 95%CI 1.005\u0026ndash;1.575) is significantly associated with diagnosed sarcopenia compared with the nonsarcopenia group. Sleep (OR\u0026thinsp;=\u0026thinsp;0.738, 95%CI 0.568\u0026ndash;0.959) and depression (OR\u0026thinsp;=\u0026thinsp;1.845, 95%CI 1.291\u0026ndash;2.638) are also significantly associated with severe sarcopenia. However, anxiety has no significant impact on different sarcopenia groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of anxiety\u003csup\u003ea\u003c/sup\u003e and different sarcopenia groups in the multiethnic population of western China (n\u0026thinsp;=\u0026thinsp;4500).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eUnadjusted model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eAdjusted\u003csup\u003ec\u003c/sup\u003e model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnosed sarcopenia\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.881\u0026ndash;1.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.865\u0026ndash;1.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere sarcopenia\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.715\u0026ndash;1.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.692\u0026ndash;1.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCI, confidence interval; OR odds ratio. \u003csup\u003ea\u003c/sup\u003eGAD-7\u0026thinsp;\u0026ge;\u0026thinsp;5 is the reference. \u003csup\u003eb\u003c/sup\u003e Nonsarcopenia is the reference. \u003csup\u003ec\u003c/sup\u003eAdjusted for age, sex, smoking history, drinking alcohol, ethnicity, education level, living alone, chronic diseases, indoor housework, and outdoor housework by logistic regression.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of depression\u003csup\u003ea\u003c/sup\u003e and different sarcopenia groups in the multiethnic population of western China (n\u0026thinsp;=\u0026thinsp;4500).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eUnadjusted model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eAdjusted model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnosed sarcopenia\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.962\u0026ndash;1.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.032\u0026ndash;1.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere sarcopenia\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.177\u0026ndash;2.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.146\u0026ndash;2.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCI, confidence interval; OR odds ratio. \u003csup\u003ea\u003c/sup\u003eGDS-15\u0026thinsp;\u0026ge;\u0026thinsp;5 is the reference. \u003csup\u003eb\u003c/sup\u003eNonsarcopenia is the reference. \u003csup\u003ec\u003c/sup\u003eAdjusted for age, sex, smoking history, drinking alcohol, ethnicity, education level, living alone, chronic diseases, indoor housework, and outdoor housework by logistic regression.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation of sleep quality, anxiety, and depression with different sarcopenia groups in the multiethnic population of western China (n\u0026thinsp;=\u0026thinsp;4500).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnosed sarcopenia\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.791\u0026ndash;1.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnxiety status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.810\u0026ndash;1.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepressive status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.005\u0026ndash;1.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere sarcopenia\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.568\u0026ndash;0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnxiety status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.633\u0026ndash;1.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepressive status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.291\u0026ndash;2.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCI, confidence interval; OR odds ratio. \u003csup\u003ea\u003c/sup\u003e Nonsarcopenia was the reference.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is based on the multiethnic population in western China using the diagnostic and classification methods recommended by the 2019 AWGS for sarcopenia. The screening process for sarcopenia used for clinical research was selected, and the following 3 groups were made: nonsarcopenia group, diagnosed sarcopenia group, and severe sarcopenia group. Research results from some European and American countries suggest that the prevalence of sarcopenia varies considerably depending on the actual methods used in various studies and the chosen cutoff values. This variation is influenced by how muscle mass is assessed using different diagnostic instruments as well as by factors including ethnicity, place of residence, and age [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. There was a 22.8% prevalence of sarcopenia in the current study; however, Taiwanese researchers screened elderly individuals\u0026thinsp;\u0026gt;\u0026thinsp;65 years of age for sarcopenia using the diagnostic criteria set by the European Working Group on Sarcopenia in Older People and reported a prevalence of 14.4% [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The prevalence of muscular dystrophy is higher in Taiwan than in other regions. Apart from differences in diagnostic criteria, this outcome may be related to the multiethnic population and lower standards of living in the western regions. Although our study included subjects over a wide age range, the incidence of muscular dystrophy was found to vary among different ethnic groups and to correlate differently with different types of muscular dystrophy. These differences among different ethnic groups may be related to genetics, dietary habits, and even religious beliefs, which require further in-depth research.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSleep quality and sarcopenia\u003c/h2\u003e \u003cp\u003eSleep quality is an important aspect required to maintain physical and mental health. Disruptions or changes in circadian rhythms are associated with the development of many chronic diseases including aging and sarcopenia. Sleep quality is a multidimensional structure that includes sleep latency, awakening after sleep onset, frequency and number of awakenings, and subjective reports of feelings and mental state upon waking. Furthermore, among older adults, it is associated with a poor quality of life, increased incidence of other diseases, and higher mortality rates [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStudies have reported that the sleep\u0026ndash;wake cycle is related to the maintenance of skeletal muscles. It plays a crucial role in numerous physiological functions, muscle structure, and metabolism of skeletal muscles. In models of disrupted circadian rhythm, the absence of the \u003cem\u003eBma/1\u003c/em\u003e gene has been reported to lead to sarcopenia and other pathological diseases of the muscle, including reduced mitochondrial density and altered mitochondrial respiration, fiber type displacement, and impaired structure of muscle segments [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The circadian clock\u0026ndash;suppressing gene \u003cem\u003eRev\u003c/em\u003e-\u003cem\u003eerbα\u003c/em\u003e, which affects sleep, also plays a crucial role in regulating skeletal muscle metabolism [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Sleep initiation and/or maintenance in elderly Japanese individuals is associated with sarcopenia [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This study found a significant correlation between sleep quality (OR: 0.717, 95%CI 0.550\u0026ndash;0.934) and severe sarcopenia but no significant correlation with diagnosed sarcopenia. In the aging population with sleep disorders, improving sleep quality may slow down muscle loss and prevent or delay the onset of severe sarcopenia.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between anxiety, depression, and sarcopenia\u003c/h2\u003e \u003cp\u003eComorbidity between anxiety and depression is very common in the elderly population; it is also common with several other physical illnesses and associated with cognitive decline [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Anxiety and depression are also related to circadian rhythms, which are closely related to skeletal muscle function [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Anxiety and depression are closely associated with fragmentation of the 24-hour activity rhythm in middleaged and elderly individuals [32]. Anxiety, depression, and physical activity are significantly correlated, with lower levels of daily activity being a core feature of mood disorders [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Currently, there is limited research on anxiety disorders; however, some studies indicate that their effects are similar to those of depression in certain aspects, such as psychomotor retardation, lower levels of daily activities, and circadian rhythm disturbances [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In this study, anxiety in the multiethnic region of western China was not significantly associated with different muscular dystrophy groups; however, depression showed significant associations with the diagnosed muscular dystrophy group and severe muscular dystrophy group.\u003c/p\u003e \u003cp\u003eDepression is characterized by low mood, slow thinking, disrupted sleep or appetite, and feelings of fatigue, and is a common mental disorder in the elderly. Some studies have reported a relationship between depression and body composition that involves factors such as skeletal muscle mass, strength, and muscle function, all of which are directly related to sarcopenia [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Both sarcopenia and depression are associated with decreased physical activity, upregulation of inflammatory factors, and hormonal dysregulation of the hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal axis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Some studies have reported that there is no significant association between myasthenia gravis and depression [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e39\u003c/span\u003e], whereas there are reports suggesting a significant association between the two [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Based on the 2019 AWGS diagnostic criteria for sarcopenia, we subdivided sarcopenia into groups and collected data on depression, which further confirmed a significant correlation between depression and both the diagnosed sarcopenia and severe sarcopenia groups.\u003c/p\u003e \u003cp\u003eOur study has some limitations. Although the PSQI is a validated and reliable measurement method, it cannot perfectly capture sleep parameters as effectively as polysomnography, which is the gold standard. Additionally, for subjects with anxiety and depression, further analyses are needed to determine if their medication status may have intervened with muscle function.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn the multiethnic population in western China, a significant correlation between sleep and severe muscle wasting syndrome, depression, and diagnosed muscle wasting syndrome and severe muscle wasting syndrome was found, but no significant correlation between anxiety and muscle wasting syndrome was noted after stratification. Preventing depression or intervention for depression in this population may be an approach to delay or reduce the occurrence of muscle wasting syndrome and may also help formulate specific medical policies. Further longitudinal studies are needed to confirm the relationship between sleep, anxiety, depression, and muscle wasting syndrome.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGAD-7\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneralized anxiety disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGDS-15\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e15-item Geriatric Depression Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSQI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePittsburgh Sleep Quality Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWCHAT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWest China Health and Aging Trend.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of West China Hospital, Sichuan University (reference: 2017\u0026thinsp;\u0026minus;\u0026thinsp;445). This study was registered at the China Clinical Trial Center (Registration Number: ChiCTR1800018895). All participants signed the informed consent before participating.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSupported by the National Key R\u0026amp;D Program of China (2018YFC2000305, 2020YFC2005600, 2020YFC2005602 and 2020YFC0840101); 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (ZYGD20010 and ZY2017201); Geriatric Health Care and Medical Research Center, Sichuan University, Chengdu, Sichuan Province, China; Sichuan Science and Technology Program (No. 2023NSFSC1158); Project funded by China Postdoctoral Science Foundation (No.2023M732473); National Clinical Research Center for Geriatrics, West China Hospital (No. Z2024JC006）. Chengdu Science and Technology Bureau Major Science and Technology Application Demonstration Project (2019YF0900083SN); The financial sponsors had no role in the design, implementation, analyses, or reporting of the results.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBRD designed research; ZGX, XLL, NH, GCZ, XLJ, XX, FJH, MLG conducted research; ZGX analyzed data; and ZGX and XLL wrote the paper. ZGX\u003c/p\u003e\n\u003cp\u003ehad primary responsibility for final content. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all study participants and their families for their cooperation in the research team.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eFang EF, Scheibye-Knudsen M, Jahn HJ, et al. A research agenda for aging in China in the 21st century. 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Indian J Psychol Med. 2020 Jul;42(6):549-554.\u003c/li\u003e\n \u003cli\u003eMoon JH, Kong MH, Kim HJ. Low Muscle Mass and Depressed Mood in Korean Adolescents: a Cross-Sectional Analysis of the Fourth and Fifth Korea National Health and Nutrition Examination Surveys. J Korean Med Sci. 2018;33(50):e320.\u003c/li\u003e\n \u003cli\u003eHallgren M, Herring MP, Owen N, et al. Exercise, Physical Activity, and Sedentary Behavior in the Treatment of Depression: Broadening the Scientific Perspectives and Clinical Opportunities. Front Psychiatry. 2016;7:36.\u003c/li\u003e\n \u003cli\u003ePatino-Hernandez D, David-Pardo DG, Borda MG, et al. Association of Fatigue With Sarcopenia and its Elements: A Secondary Analysis of SABE-Bogot\u0026aacute;. Gerontol Geriatr Med. 2017;3:2333721417703734.\u003c/li\u003e\n \u003cli\u003eChang KV, Hsu TH, Wu WT, et al. Is sarcopenia associated with depression? A systematic review and meta-analysis of observational studies. Age Ageing. 2017;46(5):738-746. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sarcopenia, Western China, Multiethnic, Sleep quality, Anxiety, Depression","lastPublishedDoi":"10.21203/rs.3.rs-4370867/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4370867/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSarcopenia not only leads to impaired physical function but may also be associated with changes in sleep and mental health as individuals age. Research on the relationship between sleep, anxiety, and depression and adultonset sarcopenia is limited; however, there are no reports indicating the relationship between them and the different groups of sarcopenia. The aim of this study is to explore the correlation between sarcopenia (diagnosed sarcopenia, severe sarcopenia) and sleep, anxiety, and depression in different groups in the multiethnic region of western China based on the 2019 Asian sarcopenia diagnostic criteria.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe diagnostic method recommended by the Asian Working Group for Sarcopenia in 2019 was used to screen for sarcopenia. The population in the multiethnic region of western China included in this study underwent bioelectrical impedance analysis to classify sarcopenia into the diagnosed sarcopenia and severe sarcopenia groups, while also recording other data for analysis. The Pittsburgh Sleep Quality Index, the 7-item Generalized Anxiety Disorder Questionnaire, and the 15-item geriatric depression scale were used to assess the sleep quality, anxiety, and depression status of participants, respectively. Multiple logistic regression multivariate analysis was used to determine the relationship among sleep, anxiety, depression, and the different types of sarcopenia.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the 4500 participants surveyed in the western region of China, 408 (9.06%) were identified as having myasthenia gravis and 618 (13.73%) as having severe myasthenia gravis, whereas 2015 individuals (44.78%) had poor sleep quality, 842 (18.71%) had anxiety, and 1045 (23.22%) had depression. Sleep abnormalities were associated with severe sarcopenia (odds ratio [OR]: 0.717, 95% confidence interval [CI] 0.550\u0026ndash;0.934), whereas depression was associated with diagnosed sarcopenia (OR: 1.289, 95%CI 1.032\u0026ndash;1.608) and severe sarcopenia (OR: 1.622, 95%CI 1.032\u0026ndash;1.608).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe western region of China is a multiethnic area with 44.78% of participants\u0026thinsp;\u0026gt;\u0026thinsp;50 years of age experiencing poor sleep quality, 18.71% suffering from anxiety, and 23.22% experiencing depression. It may be possible to delay or reduce the severity of sarcopenia by early intervention in improving sleep quality and alleviating depression.\u003c/p\u003e\u003ch2\u003eClinical trial number\u003c/h2\u003e \u003cp\u003e: ChiCTR1800018895\u003c/p\u003e","manuscriptTitle":"Correlation between sleep disorder, anxiety, depression, and sarcopenia in multiethnic areas of western China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-05 17:35:04","doi":"10.21203/rs.3.rs-4370867/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e3b99a7e-be3f-4eed-9e33-90f49ae316bf","owner":[],"postedDate":"June 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-29T16:42:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-05 17:35:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4370867","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4370867","identity":"rs-4370867","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}