Correlation of Physical Activity and Sleep Quality with Metabolic-Associated Fatty Liver Disease in Community-Based Older Adults

Correlation of Physical Activity and Sleep Quality with Metabolic-Associated Fatty Liver Disease in Community-Based Older Adults | 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 of Physical Activity and Sleep Quality with Metabolic-Associated Fatty Liver Disease in Community-Based Older Adults Mingming Huang, Qi Yousheng, xinbi Zhang, Leiming Di, Haiyuan Zhou, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6143971/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 Purpose: Physical inactivity has been identified as a potential risk factor for metabolic-associated fatty liver disease (MAFLD) in the elderly. However, the specific effects of different types and intensities of physical activity on MAFLD risk remain unclear. This study aims to examine the correlation between the type and level of physical activity and the prevalence of MAFLD in an elderly community-based population. Methods: A cross-sectional study was conducted among 815 older adults aged 65 years and above. Participants' demographic and anthropometric data were collected through field assessments and questionnaires. Body composition was measured using InBody720 (Biospace, Korea), while fatty liver diagnosis was performed via ultrasound, and liver fat content and elasticity were assessed using FibroScan. Physical activity levels were evaluated using the Physical Activity Scale for the Elderly (PASE). Binary logistic regression models were employed to analyze the relationship between various types and intensities of physical activity and MAFLD prevalence. Results: Among the 815 participants, 396 were diagnosed with MAFLD. After adjusting for confounding variables, higher levels of total physical activity (OR: 0.99, 95% CI: 0.98–0.99, P < 0.001), leisure-related physical activity (OR: 0.98, 95% CI: 0.98–0.98, P < 0.001), and occupational-related physical activity (OR: 0.99, 95% CI: 0.98–0.99, P < 0.001) were significantly associated with a lower risk of MAFLD. In contrast, home-related physical activity (OR: 0.99, 95% CI: 0.98–0.99, P = 0.103) showed no significant association.When total physical activity was categorized into four levels—light (LPA), moderate (MPA), moderate-to-vigorous (MVPA), and vigorous (VPA)—MPA was not significantly associated with MAFLD risk (OR: 0.71, 95% CI: 0.38–1.32). However, MVPA (OR: 0.24, 95% CI: 0.14–1.41) and VPA (OR: 0.05, 95% CI: 0.03–0.09) exhibited strong negative associations with MAFLD risk. Specifically, moderate-intensity leisure physical activity (LeisurePA-MPA) was significantly associated with a reduced risk of MAFLD (OR: 0.42, 95% CI: 0.21–0.87, P < 0.05), as were moderate-to-vigorous (LeisurePA-MVPA, OR: 0.24, 95% CI: 0.11–0.50, P < 0.001) and vigorous (LeisurePA-VPA, OR: 0.07, 95% CI: 0.03–0.15, P < 0.001) leisure activities. Additionally, only vigorous-intensity occupational physical activity (Occupation-VPA) was significantly associated with a reduced risk of MAFLD (OR: 0.47, 95% CI: 0.28–0.77, P < 0.001). Conclusion: Higher levels of total, leisure-related, and occupational-related physical activity are significantly associated with a lower risk of MAFLD in older adults, whereas home-related physical activity shows no significant effect. In terms of activity intensity, MVPA and VPA demonstrate strong protective effects against MAFLD, particularly in leisure and occupational settings. These findings suggest that older adults should engage in moderate-to-vigorous intensity leisure and occupational activities to effectively reduce MAFLD risk. metabolism-associated fatty liver disease MAFLD physical activity sleep 1 Introduction Metabolic-associated fatty liver disease (MAFLD) is a form of metabolic stress-induced liver injury strongly associated with insulin resistance (IR) and genetic predisposition. It is currently recognized as the most prevalent chronic liver disease worldwide [ 1 ] . The global prevalence of MAFLD has reached 38.77% and is expected to exceed 55% by 2040, correlating significantly with rising obesity rates [ 2 ] . The disease spectrum ranges from simple steatosis, characterized by fat accumulation without inflammation or fibrosis, to advanced-stage fibrosis [ 3 ] . Moreover, MAFLD is frequently accompanied by comorbidities such as type 2 diabetes [ 4 ] , hypertension [ 5 ] , obesity [ 6 ] , and dyslipidemia [ 7 ] , imposing a substantial burden on both affected individuals and healthcare systems. At present, no targeted pharmacological treatments for MAFLD have been established in clinical guidelines [ 8 ][ 9 ] . Instead, lifestyle modifications, including dietary management and increased physical activity (PA), remain the cornerstone of MAFLD prevention and treatment [ 10 ] . PA is widely recognized as a crucial protective factor in the promotion of overall health and the prevention of chronic diseases. Extensive research has demonstrated the beneficial effects of various forms of PA in mitigating and preventing MAFLD. Additionally, a cross-sectional study confirmed an inverse relationship between PA levels and MAFLD prevalence, even after adjusting for confounding variables [ 11 ] . However, long-term adherence to regular exercise remains a significant challenge for many individuals, underscoring the need for sustainable strategies that integrate daily physical activity into lifestyle interventions. Despite these findings, most existing studies primarily focus on leisure-time PA, with limited research examining the relationship between different types and intensities of PA, sleep quality, and MAFLD in elderly populations. This study aims to investigate the association between various forms and levels of PA, sleep disorders, and MAFLD prevalence among older adults in Chinese geriatric communities. The findings will provide a theoretical foundation for developing effective strategies for the prevention and management of MAFLD in the elderly. 2 Objects and Methods 2.1 Subjects A total of 815 elderly people over 65 years of age were selected from a community in Beijing, of which 396 were MAFLD patients. Inclusion criteria: (1) Age ≥ 65 years old; (2) Signed informed consent. Exclusion criteria: (1) lack of necessary demographic indicators, body measurements, and laboratory indicators; (2) lack of necessary past history and medication history; (3) malignant tumors and other serious diseases of the organ system; (4) metal stents or pacemakers placed in the body, which could not be analyzed by body composition analysis. The study was approved by the Ethics Committee of Beijing You'an Hospital affiliated with Capital Medical University (Jing You Ke Lun Zi [2024] No. 008), and all the enrolled patients signed an informed consent form. 2.2 Research methods 2.2.1 General condition and body measurements The patients' age, gender, past medical history, abdominal ultrasound and other clinical data were collected, and arrangements were made to measure their height, body mass, waist circumference, hip circumference, and BMI, ASM, body fat rate, visceral fat area, basal metabolic rate and other indexes by using Inbody720 Body Composition Tester (Biospace, Korea). 2.2.2 Laboratory examination Venous blood was drawn early in the morning after 12h of fasting, and liver and kidney functions, blood lipids, blood glucose, fasting insulin and other indexes were measured, and the homeostasis model assessment of insulin resistance (HOMA-IR) was calculated, with HOMA-IR = fasting glucose (mmol/L) × fasting insulin (mmol/L). /L) × fasting insulin (µU/mL)/22.5. 2.2.3 Diagnosis of MAFLD Based on radiologic diagnosis of hepatic steatosis and the presence of any of the following three diseases [ 12 ] : 1) overweight or obesity: 1) BMI ≥ 25 kg/m2; 2) the presence of diabetes mellitus; and 3) metabolic dysregulation. Metabolic disorders are defined as two or more of the following: ① Waist circumference ≥ 102cm for men and ≥ 88cm for women; ②Blood pressure ≥ 130/85mmHg or receiving antihypertensive treatment; ③ Triglycerides ≥ 1.7mmol/L or undergoing lipid-lowering treatment; ④Pre-diabetes: fasting blood glucose level of 100-125mg/dL or glycated blood glucose protein 5.7–6.4; ⑤ HDL-C: male < 1.0 mmol/L; female 2mg/L. 2.2.4 Physical Activity Scale for Elderly Questionnaire The PASE (Physical Activity Scale for Elderly) questionnaire was used to investigate the physical activity level of the elderly in the Beijing community, which is a classic international epidemiological questionnaire on physical activity for the elderly. The PASE questionnaire consists of 10 questions and 26 issues, including leisure-related physical activity, housework-related physical activity and occupation-related physical activity. The questionnaire was used to investigate the physical activities engaged in by the respondents in the past 7 days, and the score weights and scores of different test questions were used to calculate the final physical activity scores of each item, and the total PASE scores were in the range of 0-400 points. The reliability and validity of the modified PASE questionnaire have been verified [ 13 ] . 2.3 Statistical methods SPSS 26.0 was used to analyze the data statistically. Measurement information conforming to normal distribution was described by mean ± standard deviation (x ± s), and independent samples t-test was used for comparison between two groups; non-normally distributed measurement information was described by interquartile spacing method M(P25,P75), and Mann-WhitneyU rank sum test was used for comparison between two groups. Binary logistic regression was used to analyze the correlation between type and level of physical activity and sleep status on the prevalence of metabolism-related fatty liver disease with a test level of α = 0.05. 3 Results 3.1 Baseline Characteristics A total of 815 elderly individuals aged 65 years and older (mean age: 70.48 ± 4.95 years) were recruited for this study, including 396 patients with metabolic-associated fatty liver disease (MAFLD) and 419 non-MAFLD subjects, resulting in a MAFLD prevalence rate of 48.58%. Among the MAFLD patients, 88 (22.2%) were male, and 308 (77.8%) were female, indicating a higher prevalence of MAFLD among females compared to males. Significant differences were observed between the MAFLD and non-MAFLD groups in terms of age, gender, body mass index (BMI), and smoking history ( P < 0.05). Notably, a higher BMI was associated with an increased likelihood of MAFLD. However, no significant differences were found between the two groups regarding the prevalence of hypertension, diabetes mellitus, or coronary heart disease ( P > 0.05). Further details are provided in Table 1 . Table 1 Comparison of general information between MAFLD and non-MAFLD Variable MAFLD(n = 396) Non-MAFLD(n = 419) χ 2 ∕t P Age (y) 70.04 ± 4.97 70.90 ± 4.96 t=-2.48 0.01* BMI (kg/m2) 26.99 ± 3.09 24.74 ± 2.86 t = 10.74 < 0.001** Height 160.29 ± 7.85 159.24 ± 7.89 t=-1.87 0.06 Weight 66.18 ± 10.14 65.94 ± 10.28 t=-0.32 0.74 TG (mmol/L) 1.55 ± 0.81 1.51 ± 0.96 t=-0.60 0.54 HDL (mmol/L) 1.21 ± 0.25 1.08 ± 0.23 t=-7.33 < 0.001** LDL (mmol/L) 3.36 ± 1.21 3.24 ± 0.98 t=-1.62 0.10 TC (mmol/L) 4.89 ± 1.32 4.63 ± 1.06 t=-3.06 < 0.001** Gender Male Female 88 308 189 230 Z = 47.52 < 0.001** Education level lliterate education Primary education Secondary education achelor degree Z = 0.33 0.95 36 41 76 86 244 253 31 32 high blood pressure Yes No Z = 1.94 0.16 310 316 79 102 Diabetes Yes No Z = 1.60 0.20 164 155 232 263 coronary heart disease Yes No Z = 0.31 0.57 136 154 253 264 Smoked at least 100 cigarettes Yes No Z = 34.16 < 0.001** 53 128 332 283 P < 0.05 indicates a significant difference, denoted by * , while P < 0.001 indicates a very significant difference, denoted by ** 3.2 Correlation between different types of physical activity and prevalence of MAFLD Table 2 presents the results of the binary logistic regression analysis, examining the relationship between different types of physical activity (PA) and the prevalence of metabolic-associated fatty liver disease (MAFLD) in the elderly population. The findings indicate a significant negative correlation between total PA, leisure-related PA, and occupational PA levels and the prevalence of MAFLD ( P < 0.001). After adjusting for age, gender, BMI, and other confounding variables, Models 2 and 3 confirmed that this association remained statistically significant.Additionally, an inverse correlation was observed between housework-related PA and MAFLD prevalence in the unadjusted model ( P 0.05). Table 2 Binary logistic regression results of different types of physical activity and prevalence of MAFLD Variables Model1 Model2 Model3 OR(95%CI) P OR(95%CI) P OR(95%CI) P Total PA 0.99 (0.98 ~ 0.99) < 0.001 ** 0.99 (0.98 ~ 0.99) < 0.001 ** 0.99(0.99 ~ 0.99) < 0.001 ** Leisure-time PA 0.98 (0.98 ~ 0.98) < 0.001 ** 0.98 (0.98 ~ 0.98) < 0.001 ** 0.98 (0.98 ~ 0.99) < 0.001 ** Household time-PA 0.99 (0.98 ~ 0.99) 0.040 * 0.98 (0.97 ~ 0.99) 0.010 * 0.99 (0.98 ~ 1.00) 0.103 Occupation time-PA 0.99 (0.98 ~ 0.99) < 0.001 ** 0.98 (0.98 ~ 0.99) < 0.001 ** 0.98 (0.97 ~ 0.99) < 0.001 ** P < 0.05 indicates a significant difference, denoted by * , while P < 0.001 indicates a very significant difference, denoted by ** Model 1: Crude; Model 2: Adjust: age, gender; Model 3: Adjust: Age, Gender, BMI, triglycerides, totalcholesterol, ALT, AST, AST/ALT, hypertension, coronary heart disease, diabetes, stroke, ASCVD, dyslipidemia, metabolic syndrome. 3.3 Correlation between different physical activity levels and prevalence of MAFLD The total physical activity of the elderly population was further categorized into four levels—light physical activity (LPA), moderate physical activity (MPA), moderate-to-vigorous physical activity (MVPA), and vigorous physical activity (VPA)—based on quartiles. Regression analysis revealed that MPA was not significantly associated with the risk of MAFLD (OR: 0.71, 95% CI: 0.38–1.32). In contrast, both MVPA (OR: 0.24, 95% CI: 0.14–1.41) and VPA (OR: 0.05, 95% CI: 0.03–0.09) demonstrated a significant negative correlation with MAFLD prevalence.Furthermore, after adjusting for age, gender, and BMI, Models II and III continued to show a highly significant association ( P < 0.001), reinforcing the robustness of these findings. Table 3 Binary logistic regression results of different levels of physical strength and prevalence of MAFLD Variables Model1 Model2 Model3 OR(95%CI) P OR(95%CI) P OR(95%CI) P LPA 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) MPA 0.71 (0.38 ~ 1.32) 0.276 0.71 (0.38 ~ 1.32) 0.277 0.59 (0.29 ~ 1.18) 0.138 MVPA 0.24 (0.14 ~ 0.41) < 0.001 ** 0.24 (0.14 ~ 0.41) < 0.001 ** 0.27 (0.14 ~ 0.49) < 0.001 ** VPA 0.05 (0.03 ~ 0.09) < 0.001 ** 0.05 (0.03 ~ 0.09) < 0.001 ** 0.05 (0.03 ~ 0.10) < 0.001 ** LPA,light physical activity; MPA, moderate physical activity; MVPA, moderate-to-vigorous physical activity; VPA, vigorous physical activity. P < 0.05 indicates a significant difference, denoted by*, while P < 0.001 indicates a very significant difference, denoted by ** Model 1:Crude;Model 2:Adjust: age, gender;Model 3:Adjust:Age,Gender,BMI,triglycerides,totalcholesterol,ALT,AST,AST/ALT,hypertension,coronary heart disease, diabetes, stroke,ASCVD, dyslipidemia, metabolic syndrome 3.4 Correlation between different levels of leisure/occupation-related PA and MAFLD prevalence Given the observed negative correlation between total leisure-related physical activity levels and the risk of MAFLD, a further regression analysis was conducted by categorizing leisure-related physical activity into four levels: LeisurePA-LPA, LeisurePA-MPA, LeisurePA-MVPA, and LeisurePA-VPA. The results indicated a significant negative association between LeisurePA-MPA and MAFLD prevalence (P < 0.05). Compared to LeisurePA-LPA, LeisurePA-MPA demonstrated a stronger inverse correlation with MAFLD risk, with a statistically significant difference (P < 0.05). Additionally, both LeisurePA-MVPA and LeisurePA-VPA exhibited a highly significant negative correlation with MAFLD prevalence (P < 0.001). Occupation-related physical activity was categorized into four levels: Occupation-LPA, Occupation-MPA, Occupation-MVPA, and Occupation-VPA. Binary logistic regression analysis showed no significant association between Occupation-LPA and MAFLD prevalence ( P > 0.05). A significant correlation was observed between Occupation-MVPA and MAFLD risk (P < 0.05); however, after adjusting for age and gender, Model 2 indicated that this association was no longer significant. In contrast, Occupation-VPA remained highly significantly associated with a reduced risk of MAFLD across all three models (P < 0.001). Table 4 Results of binary logistic regression of leisure-related different PA levels and prevalence of MAFLD Variables Model1 Model2 Model3 OR(95%CI) P OR(95%CI) P OR(95%CI) P Leisure PA-LPA 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) LeisurePA-MPA 0.42 (0.21 ~ 0.87) 0.020 * 0.38 (0.17 ~ 0.85) 0.019 * 0.38 (0.17 ~ 0.85) 0.019 * LeisurePA-MVPA 0.24 (0.11 ~ 0.50) < 0.001 ** 0.24 (0.10 ~ 0.59) 0.002 ** 0.24 (0.10 ~ 0.59) 0.002 ** Leisure-VPA 0.07 (0.04 ~ 0.15) < 0.001 ** 0.07 (0.03 ~ 0.15) < 0.001 ** 0.07 (0.03 ~ 0.15) < 0.001 ** LPA,light physical activity; MPA, moderate physical activity; MVPA, moderate-to-vigorous physical activity; VPA, vigorous physical activity. P < 0.05 indicates a significant difference, denoted by * , while P < 0.001 indicates a very significant difference, denoted by ** Table 5 Results of binary logistic regression of occupation-related different levels of physical activity and risk of MAFLD prevalence Variables Model1 Model2 Model3 OR(95%CI) P OR(95%CI) P OR(95%CI) P occupation-LPA 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) LeisurePA-MPA 0.79(0.60 ~ 0.82) 0.212 0.88(0.54 ~ 1.15) 0.323 0.88(0.54 ~ 1.15) 0.323 occupation-MVPA 0.66 (0.45 ~ 0.97) 0.332 0.72 (0.43 ~ 1.20) 0.206 0.72 (0.43 ~ 1.20) 0.206 occupation-VPA 0.57 (0.40 ~ 0.83) 0.003 ** 0.47 (0.28 ~ 0.77) 0.003 ** 0.47 (0.28 ~ 0.77) 0.003 ** LPA,light physical activity; MPA, moderate physical activity; MVPA, moderate-to-vigorous physical activity; VPA, vigorous physical activity P < 0.05 indicates a significant difference, denoted by * , while P < 0.001 indicates a very significant difference, denoted by ** 3.3 Relationship between sleep duration as well as sleep quality and MAFLD This study examined the relationship between sleep duration, sleep disorders, and the risk of MAFLD prevalence in the elderly population. The findings indicated no significant association between sleep duration within the range of 6–10 hours and MAFLD prevalence when compared to a sleep duration of less than 6 hours ( P > 0.05).Similarly, correlation analysis between sleep disorders and MAFLD prevalence revealed no significant associations between the absence of sleep disorders, difficulty falling asleep, early wakefulness, frequent dreaming, or sleepwalking and the risk of MAFLD ( P > 0.05). Table 6 Binary logistic regression results of sleep duration and risk of MAFLD prevalence Variables Model1 Model2 Model3 OR(95%CI) P OR(95%CI) P OR(95%CI) P 6h 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 6-10h 0.78 (0.57 ~ 1.05) 0.100 0.78 (0.57 ~ 1.05) 0.101 0.87 (0.60 ~ 1.26) 0.265 Model 1:Crude;Model 2:Adjust: age, gender;Model 3:Adjust:Age,Gender,BMI,triglycerides,totalcholesterol,ALT,AST,AST/ALT,hypertension,coronary heart disease, diabetes, stroke,ASCVD, dyslipidemia, metabolic syndrome Table 7 Binary logistic regression results of sleep disorders and risk of MAFLD prevalence Variables Model1 Model2 Model3 OR(95%CI) P OR(95%CI) P OR(95%CI) P NSD 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) DFA 1.34 (0.94 ~ 1.92) 0.109 1.34 (0.94 ~ 1.92) 0.107 0.00 (0.00 ~ Inf) 0.999 EMA 1.43 (0.63 ~ 3.25) 0.398 1.43 (0.63 ~ 3.26) 0.394 0.00 (0.00 ~ Inf) 0.999 FD 0.58 (0.19 ~ 1.75) 0.332 0.58 (0.19 ~ 1.76) 0.340 0.00 (0.00 ~ Inf) 0.998 SW 0.50 (0.07 ~ 3.55) 0.485 0.49 (0.07 ~ 3.51) 0.475 0.00 (0.00 ~ Inf) 0.999 Model 1:Crude;Model 2:Adjust: age, gender;Model 3:Adjust:Age,Gender,BMI,triglycerides,totalcholesterol,ALT,AST,AST/ALT,hypertension,coronary heart disease, diabetes, stroke,ASCVD, dyslipidemia, metabolic syndrome NSD,No sleep disorders;DFA,Difficulty falling asleep;EMA,Early morning awakening FD,Frequent dreaming;SW,Sleepwalking 4 Discussion In this cross-sectional study, we investigated the relationship between physical activity, sleep, and the prevalence of MAFLD in the elderly population. Several key findings emerged. First, total physical activity, recreational physical activity, and occupational physical activity were all negatively correlated with MAFLD prevalence in older adults.Further analysis of different physical activity levels revealed that both MVPA and vigorous PA were significantly associated with a reduced risk of MAFLD. When physical activity types were further categorized by intensity, a negative correlation was observed between various levels of recreational physical activity, as well as high-intensity occupational physical activity, and MAFLD prevalence.However, regarding sleep, neither sleep duration nor different types of sleep disorders were found to be significantly associated with MAFLD prevalence. Physical activity and sleep are widely recognized as key lifestyle factors influencing the risk of MAFLD [ 14 ] . A growing body of epidemiological research has demonstrated a strong correlation between physical inactivity and adverse health outcomes, including an increased risk of MAFLD and cardiovascular disease [ 15 ][ 16 ] . Regular physical activity plays a crucial role in maintaining metabolic health, supporting cardiovascular function, and regulating systemic inflammation [ 17 ] .However, in recent years, global physical inactivity has risen significantly, with more than 25% of the population reportedly failing to meet the World Health Organization’s recommended physical activity levels [ 18 ] . Epidemiological studies have consistently linked physical inactivity to chronic conditions such as obesity, metabolic syndrome, and MAFLD [ 19 ] . Increasing physical activity levels has been identified as an effective intervention for improving MAFLD outcomes [ 20 ] . A systematic review and meta-analysis by Zelber-Sagi et al. reported that higher levels of physical activity were associated with a significantly lower risk of developing MAFLD [ 21 ] . Moreover, regular moderate-to-high-intensity physical activity was shown to reduce hepatic steatosis and lower the risk of hepatic inflammation and fibrosis [ 22 ] .Sleep also plays a critical role in health, as individuals spend approximately one-third of their lives asleep. High-quality sleep is essential for cardiovascular health and the regulation of endocrine and immune functions. However, in recent decades, the prevalence of short sleep duration (defined as < 6 hours) has exceeded 20% [ 23 ] . Epidemiological studies have demonstrated associations between insufficient sleep duration and conditions such as obesity, metabolic syndrome, and cardiovascular disease [ 24 ] . Furthermore, research has identified short sleep duration as a potential risk factor for MAFLD [ 25 ] . A meta-analysis by Wijarnpreecha et al., which included six studies, found a significant association between short sleep duration and an increased risk of MAFLD [ 26 ] . The primary finding of this study confirms that total physical activity levels in elderly individuals serve as a protective factor against MAFLD. Furthermore, the prevalence of MAFLD in this population was significantly associated with leisure and occupational physical activity. These results align with previous research.Seungho Ryu conducted a cross-sectional study on the association between sedentary time, physical activity levels, and nonalcoholic fatty liver disease (NAFLD) in a cohort of 139,056 South Koreans [ 27 ] . After adjusting for potential confounders, including sedentary time, total calorie intake, smoking, and alcohol consumption, a significant inverse relationship between physical activity levels and MAFLD prevalence remained evident (P < 0.001). These findings are consistent with those of the present study.Similarly, Donghee Kim investigated the impact of physical activity on NAFLD in a cohort of 24,588 middle-aged adults in the United States (mean age: 47 years) [ 28 ] . The study found that both recreational and transportation-related physical activity had a significant protective effect against NAFLD, whereas occupational physical activity showed no significant association with MAFLD. This variation may be attributed to differences in occupational activity levels across age groups.In older adults, the health benefits of light physical activity are more pronounced due to lower metabolic efficiency. Aging leads to a decreased basal metabolic rate, making even light-to-moderate occupational activity effective in improving energy balance and reducing visceral fat accumulation, thereby lowering MAFLD prevalence. In contrast, middle-aged adults, who have a higher basal metabolic rate, require more intensive physical activity to achieve similar health benefits, which may explain why high-intensity occupational activity appears less protective in this group.Moreover, occupational activities among older adults tend to be of low to moderate intensity and moderate duration, such as light labor or supportive work. These activity levels align with health-promoting exercise intensities and contribute to improved insulin sensitivity, enhanced energy metabolism, and better fat distribution, thereby offering protection against MAFLD. Conversely, high-intensity occupational tasks, such as prolonged standing, heavy lifting, and mechanized operations, may increase energy expenditure but can also lead to chronic fatigue, metabolic stress, and inflammatory responses, potentially diminishing their protective effects against MAFLD.This discrepancy underscores the need for targeted intervention strategies that consider the type and characteristics of occupational activities to effectively prevent and manage MAFLD across different age groups. This study also found that different levels of leisure-related and occupational-related physical activity were differentially associated with MAFLD prevalence in the elderly population. Compared to low-intensity leisure-related physical activity, moderate-intensity, moderate-to-vigorous-intensity, and high-intensity leisure-related physical activity were all significantly associated with a reduced prevalence of MAFLD. However, when analyzing different levels of occupational-related physical activity, only high-intensity occupational activity demonstrated a significant association with MAFLD prevalence.Chinese scholar Bing Renjie conducted a study using a leisure physical activity questionnaire to assess the activity levels of 1,124 older adults and explored the impact of physical activity on MAFLD prevalence [ 29 ] . The findings indicated that increasing leisure-time physical activity significantly reduced the risk of MAFLD in older adults. Similarly, a meta-analysis examining the relationship between physical activity and MAFLD risk reached the same conclusion. The analysis reported that the highest levels of physical activity were associated with a lower risk of MAFLD compared to the lowest levels, with a risk ratio (RR) of 0.82 for every additional 500 MET-minutes of physical activity per week [ 30 ] . The study concluded that engaging in at least 500 MET-minutes of physical activity per week may reduce the risk of developing MAFLD.The protective effects of different levels of leisure-related physical activity in reducing MAFLD risk may be attributed to its diverse forms, voluntary nature, psychological benefits, and association with overall healthy lifestyle behaviors. In contrast, only high-intensity occupational physical activity significantly reduced the risk of MAFLD, while low- and moderate-intensity occupational activities did not exhibit similar protective effects. This may be due to insufficient energy expenditure, the repetitive nature of occupational tasks, and potentially negative metabolic effects. These findings suggest that older adults should be encouraged to increase their leisure-time physical activity to effectively reduce the risk of MAFLD. In addition, this study did not find a significant correlation between sleep duration, sleep quality, and MAFLD in older adults. Previous research has suggested that sleep quality may be a risk factor for MAFLD.A cross-sectional study involving 4,828 participants examined the association between sleep quality and MAFLD after adjusting for age, weight, smoking history, and physical activity. The study found that sleep quality was associated with MAFLD prevalence, with notable gender differences [ 31 ] . Similarly, Um Y. J. conducted a four-year cohort study investigating the relationship between sleep duration, sleep quality, and MAFLD prevalence [ 32 ] . This study evaluated sleep patterns in 143,306 Korean adults without MAFLD (mean age: 36.6 years) and followed them for an average of four years. The findings indicated that short sleep duration was independently associated with an increased risk of developing MAFLD, suggesting that sleep deprivation contributes to both the risk and severity of the disease.However, it is important to consider that sleep duration and quality generally decline with age. Older adults often experience increased sleep fragmentation and reduced deep sleep, which may minimize individual differences and obscure the impact of sleep on MAFLD. Additionally, older adults may compensate for insufficient nighttime sleep through daytime naps or other forms of rest, potentially mitigating the negative effects of poor sleep on metabolic health. Physical activity plays a crucial role in the prevention and management of MAFLD through multiple mechanisms, including energy expenditure, insulin resistance, inflammation, and oxidative stress. Regular physical activity enhances energy expenditure, promotes fat oxidation and metabolism, and reduces body fat accumulation, thereby lowering the risk of MAFLD [ 33 ] . Additionally, physical activity serves as an effective therapeutic approach for individuals with MAFLD. Studies have shown that a 5–10% reduction in body weight can significantly improve hepatic steatosis and inflammation, while a weight loss exceeding 10% can reduce hepatic fibrosis.Moreover, research suggests that improvements in lipid metabolism due to exercise are independent of body weight and play a key role in reducing hepatic lipid deposition [ 34 ] . Romero et al. found that regular physical activity, even without changes in body weight, led to reductions in hepatic fat, improved serum liver enzyme levels, and enhanced hepatic fatty acid oxidation [ 35 ] . Another mechanism by which physical activity benefits individuals with MAFLD is its ability to reduce systemic inflammation. This effect is partially attributed to muscle-derived factors, including cytokines and other peptides secreted by muscle fibers, which exert paracrine and endocrine functions. These substances, released in response to muscle contractions, may have both direct anti-inflammatory effects and indirect effects on fat metabolism, ultimately reducing the risk of MAFLD.In contrast, the mechanisms underlying the relationship between sleep disorders and MAFLD prevalence remain unclear. It has been suggested that the hypothalamic-pituitary-adrenal (HPA) axis and autonomic nervous system activity play a vital role in regulating immune and cardiometabolic functions [ 36 ] . Poor sleep quality activates the HPA axis, leading to increased secretion of stress hormones such as cortisol and catecholamines, which may contribute to a higher risk of metabolic syndrome. Additionally, insufficient sleep duration has been shown to increase appetite by elevating ghrelin (the hunger hormone) and reducing leptin levels, ultimately leading to weight gain and obesity—both of which are risk factors for MAFLD. Furthermore, sleep deprivation has been linked to impaired insulin sensitivity [ 37 ] , with insulin resistance being a key factor in the pathogenesis of MAFLD. This study has several limitations. First, as it employed a cross-sectional design, it was not possible to establish a causal relationship between physical activity, sleep, and MAFLD. Second, due to the large sample size, physical activity levels in the elderly population were assessed using self-reported questionnaires rather than accelerometers, which may have introduced measurement bias due to the subjective nature of the responses. Lastly, although the study adjusted for multiple potential confounders, unaccounted variables such as dietary habits and genetic predisposition may also influence MAFLD risk. In conclusion, this study found that moderate-to-high-intensity physical activity was significantly associated with a lower risk of MAFLD, while poorer sleep quality (e.g., difficulty falling asleep and excessive dreaming) was linked to a higher risk. These findings suggest that increasing physical activity levels and improving sleep quality may help reduce MAFLD risk in older adults. Declarations Acknowledgements Not applicable. Author contributions H.M.M., Q.Y.S., and Z.X.B. wrote the main manuscript text. D.L.M., Z.H.Y., and J.H.Y. collected the data. W.Q. and Y.R.X. revised and edited the manuscript. W.Z., W.J., and Z.J. organized and processed the data. All authors reviewed and approved the final manuscript. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Declarations Ethics approval and consent to participate The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of the Ethics Committee of Beijing You'an Hospital affiliated with Capital Medical University (Jing You Ke Lun Zi [2024] No. 008). Consent for publication Not applicable. Competing interests The authors declare no competing interests. References LiJ,ZouB,YeoYH,etal.Prevalence,incidence,and outcome of non-alcoholic fatty liver disease in Asia,1999–2019:a system aticre view and Meta-analysis[J]. Lancet Gastroenterol Hepatol,2019,4(5):389–398. Younossi, Zobair M., Markos Kalligeros, and Linda Henry. "Epidemiology of Metabolic Dysfunction Associated Steatotic Liver Disease." Clinical and Molecular Hepatology (2024). Eslam, Mohammed, et al. "Metabolic (dysfunction)-associated fatty liver disease in individuals of normal weight." Nature reviews Gastroenterology & hepatology 19.10 (2022): 638–651. Adams, L.A.; Anstee, Q.M.; Tilg, H.; Targher, G. Non-alcoholic fatty liver disease and its relationship with cardiovascular disease and other extrahepatic diseases. Gut 2017, 66, 1138–1153. Younossi, Z.M. Non-alcoholic fatty liver disease—A global public health perspective. J. Hepatol. 2019, 70, 531–544. Umbro, I.; Baratta, F.; Angelico, F.; Del Ben, M. Nonalcoholic fatty liver disease and the kidney: A review. Biomedicines 2021,9, 1370. Chalasani, N.; Younossi, Z.; Lavine, J.E.; Charlton, M.; Cusi, K.; Rinella, M.; Harrison, S.A.; Brunt, E.M.; Sanyal, A.J. The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases. Hepatology 2018, 67, 328–357. Dyson, J.K.; Anstee, Q.M.; McPherson, S. Non-alcoholic fatty liver disease: A practical approach to treatment. Frontline Gastroenterol. 2014, 5, 277–286. AnguloP,HuiJM,MarchesiniG,etal.TheMAFLDfibrosis score:A noninvasive system that identifies liver fibrosisin patients with MAFLD.Hepatology,2007,45(4):846–854. Lavi, Einav Sheinman. The Association of Physical Activity With the Incidence and Progression of NAFLD and Liver Fibrosis. MS thesis. University of Haifa (Israel), 2023. Kim, Donghee, et al. "Physical activity, measured objectively, is associated with lower mortality in patients with nonalcoholic fatty liver disease." Clinical Gastroenterology and Hepatology 19.6 (2021): 1240–1247. Liu, Qiling, et al. "A comparison of NAFLD and MAFLD diagnostic criteria in contemporary urban healthy adults in China: a cross-sectional study." BMC gastroenterology 22.1 (2022): 471. Yu Hongjun, Qiu Jun. Reliability and validity of the Physical Activity Scale for the Elderly (PASE) in measuring physical activity among Chinese older adults [J]. Journal of Shanghai University of Sport, 2014, 38(5): 45. Irwin, M.R.; Olmstead, R.; Carroll, J.E. Sleep disturbance, sleep duration, and inflammation: A Systematic review and meta analysis of cohort studies and experimental sleep deprivation. Biol. Psychiatry 2016, 80, 40–52. Grandner, M.A.; Sands-Lincoln, M.R.; Pak, V.M.; Garland, S.N. Sleep duration, cardiovascular disease, and proinflammatory biomarkers. Nat. Sci. Sleep 2013, 5, 93–107. Sheehan, C.M.; Frochen, S.E.; Walsemann, K.M.; Ailshire, J.A. Are U.S. adults reporting less sleep?: Findings from sleep duration trends in the National Health Interview Survey, 2004–2017. Sleep 2019, 42, zsy221. Warburton, D.E.R.; Nicol, C.W.; Bredin, S.S.D. Health benefits of physical activity: The evidence. CMAJ 2006, 174, 801–809. Guthold, R.; Stevens, G.A.; Riley, L.M.; Bull, F.C. Worldwide trends in insufficient physical activity from 2001 to 2016: A pooled analysis of 358 population-based surveys with 1.9 million participants. Lancet Glob. Health 2018, 6, e1077-e1086. Donnelly, J.E.; Blair, S.N.; Jakicic, J.M.; Manore, M.M.; Rankin, J.W.; Smith, B.K. Appropriate physical activity intervention strategies for weight loss and prevention of weight regain for adults. Med. Sci. Sports Exerc. 2009, 41, 459–471 Jia Guoyu. Randomized controlled study on the intervention effects of different exercise modalities on non-alcoholic fatty liver disease [D]. Tianjin Medical University, 2021. Zelber-Sagi, S.; Nitzan-Kaluski, D.; Goldsmith, R.; et al. Role of physical activity in nonalcoholic fatty liver disease: A systematic review and meta-analysis. Hepatology 2007, 48, 1794–1805. Xi, B.; He, D.; Zhang, M.; Xue, J.; Zhou, D. Short sleep duration predicts risk of metabolic syndrome: A systematic review and meta-analysis. Sleep Med. Rev. 2014, 18, 293–297. Kim CW༌Yun K༌Jung HS༌et al༎ Sleep duration and quality in relation to non ། alcoholic fatty liver disease in middle ། aged workers and their spouses༻J༽༎ Journal of Hepatology༌2013༌59(2) : 351 ། 357༎ Rinella, M.E. Nonalcoholic fatty liver disease: A systematic review. JAMA 2015, 313, 2263–2273. Liu Lifeng. Association analysis between sleep duration and metabolically associated fatty liver disease: A cross-sectional study [D]. Huazhong University of Science and Technology, Wuhan, 2014. Wijarnpreecha K༌Thongprayoon C༌Panjawatanan P༌et al༎Short sleep duration and risk of nonalcoholic fatty liver disease: Asystematic review and meta།analysis༻J༽༎Journal of Gastroenterology and Hepatology༌2016༌31( 11) : 1802 ། 1807༎ Slavish, D.C.; Taylor, D.J.; Lichstein, K.L. Intraindividual variability in sleep and comorbid medical and mental health conditions.Sleep 2019, 42, zsz052. Kim, Donghee, et al. "Inadequate physical activity and sedentary behavior are independent predictors of nonalcoholic fatty liver disease." Hepatology 72.5 (2020): 1556–1568. Bing Renjie, Wang Yubo, Zhou Kaixiang, Bao Dapeng. A cross-sectional study on the relationship between leisure-time physical activity levels and non-alcoholic fatty liver disease in the elderly population of Tianjin [A]. Proceedings of the 13th National Sports Science Conference—Special Report (Sports Medicine Division) [C]. Chinese Society of Sports Science, 2023: 3. Bose, M.; Olivan, B.; Laferrere, B. Stress and obesity: The role of the hypothalamic-pituitary-adrenal axis in metabolic disease.Curr. Opin. Endocrinol. Diabetes Obes. 2009, 16, 340–346. Takahashi A, Anzai Y, Kuroda M, et al. Effects of sleep quality on non-alcoholic fatty liver disease: a cross-sectional survey[J]. BMJ open, 2020, 10(10). Um Y J, Chang Y, Jung H S, et al. Sleep duration, sleep quality, and the development of nonalcoholic fatty liver disease: a cohort study[J]. Clinical and translational gastroenterology, 2021, 12(10). E. Teixeira de Lemos et al. "Regular Physical Exercise as a Strategy to Improve Antioxidant and Anti-Inflammatory Status: Benefits in Type 2 Diabetes Mellitus." Oxidative Medicine and Cellular Longevity, 2012 (2012). Hirokazu Takahashi et al. "Therapeutic Approaches to Nonalcoholic Fatty Liver Disease: Exercise Intervention and Related Mechanisms." Frontiers in Endocrinology, 9 (2018). A. Onyango et al. "Cellular Stresses and Stress Responses in the Pathogenesis of Insulin Resistance." Oxidative Medicine and Cellular Longevity, 2018 (2018). Buckley, T.M.; Schatzberg, A.F. On the interactions of the hypothalamic-pituitary-adrenal (HPA) axis and sleep: Normal HPA axis activity and circadian rhythm, exemplary sleep disorders. J. Clin. Endocrinol. Metab. 2005, 90, 3106–3114. Briancon-Marjollet, A.; Weiszenstein, M.; Henri, M.; Thomas, A.; Godin-Ribuot, D.; Polak, J. The impact of sleep disorders on glucose metabolism: Endocrine and molecular mechanisms. Diabetol. Metab. Syndr. 2015, 7, 25. 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6143971","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":425555045,"identity":"16fc376f-c57d-4175-b934-fe4011413205","order_by":0,"name":"Mingming Huang","email":"","orcid":"","institution":"Capital University of Physical Education and Sports","correspondingAuthor":false,"prefix":"","firstName":"Mingming","middleName":"","lastName":"Huang","suffix":""},{"id":425555046,"identity":"2f51d506-eca1-4707-bd25-5cfc8375d931","order_by":1,"name":"Qi Yousheng","email":"","orcid":"","institution":"You An Men Community Medical Service Center of Fengtai District","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Yousheng","suffix":""},{"id":425555050,"identity":"f59983b2-afe3-4951-9221-6fd9c100aa2e","order_by":2,"name":"xinbi Zhang","email":"","orcid":"","institution":"Capital University of Physical Education and Sports","correspondingAuthor":false,"prefix":"","firstName":"xinbi","middleName":"","lastName":"Zhang","suffix":""},{"id":425555051,"identity":"ebf6762a-0c2a-4566-bc0f-eee054600cb2","order_by":3,"name":"Leiming Di","email":"","orcid":"","institution":"Capital University of Physical Education and Sports","correspondingAuthor":false,"prefix":"","firstName":"Leiming","middleName":"","lastName":"Di","suffix":""},{"id":425555052,"identity":"f2ec0356-5cb1-4751-9c53-f3c9671a3df3","order_by":4,"name":"Haiyuan Zhou","email":"","orcid":"","institution":"Capital University of Physical Education and 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Hospital, Capital MedicalUniversity","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zhang","suffix":""},{"id":425555067,"identity":"ab2844fd-31be-4b8d-a044-b4a06dbcad8b","order_by":10,"name":"Jian Wu","email":"","orcid":"","institution":"Capital University of Physical Education and Sports","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-03-03 08:08:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6143971/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6143971/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78224347,"identity":"ba242aee-8662-4fa2-8f62-b0554411718c","added_by":"auto","created_at":"2025-03-11 06:47:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1165986,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6143971/v1/e868f9dd-b541-47d7-a38b-6a37fa570ddc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlation of Physical Activity and Sleep Quality with Metabolic-Associated Fatty Liver Disease in Community-Based Older Adults","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eMetabolic-associated fatty liver disease (MAFLD) is a form of metabolic stress-induced liver injury strongly associated with insulin resistance (IR) and genetic predisposition. It is currently recognized as the most prevalent chronic liver disease worldwide\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e1\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. The global prevalence of MAFLD has reached 38.77% and is expected to exceed 55% by 2040, correlating significantly with rising obesity rates\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e2\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. The disease spectrum ranges from simple steatosis, characterized by fat accumulation without inflammation or fibrosis, to advanced-stage fibrosis\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e3\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Moreover, MAFLD is frequently accompanied by comorbidities such as type 2 diabetes\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e4\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, hypertension\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e5\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, obesity\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e6\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, and dyslipidemia\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e7\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, imposing a substantial burden on both affected individuals and healthcare systems.\u003c/p\u003e \u003cp\u003eAt present, no targeted pharmacological treatments for MAFLD have been established in clinical guidelines\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e8\u003c/sup\u003e\u003csup\u003e][\u003c/sup\u003e\u003csup\u003e9\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Instead, lifestyle modifications, including dietary management and increased physical activity (PA), remain the cornerstone of MAFLD prevention and treatment\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e10\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. PA is widely recognized as a crucial protective factor in the promotion of overall health and the prevention of chronic diseases. Extensive research has demonstrated the beneficial effects of various forms of PA in mitigating and preventing MAFLD. Additionally, a cross-sectional study confirmed an inverse relationship between PA levels and MAFLD prevalence, even after adjusting for confounding variables\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e11\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. However, long-term adherence to regular exercise remains a significant challenge for many individuals, underscoring the need for sustainable strategies that integrate daily physical activity into lifestyle interventions.\u003c/p\u003e \u003cp\u003eDespite these findings, most existing studies primarily focus on leisure-time PA, with limited research examining the relationship between different types and intensities of PA, sleep quality, and MAFLD in elderly populations. This study aims to investigate the association between various forms and levels of PA, sleep disorders, and MAFLD prevalence among older adults in Chinese geriatric communities. The findings will provide a theoretical foundation for developing effective strategies for the prevention and management of MAFLD in the elderly.\u003c/p\u003e"},{"header":"2 Objects and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Subjects\u003c/h2\u003e \u003cp\u003eA total of 815 elderly people over 65 years of age were selected from a community in Beijing, of which 396 were MAFLD patients. Inclusion criteria: (1) Age\u0026thinsp;\u0026ge;\u0026thinsp;65 years old; (2) Signed informed consent. Exclusion criteria: (1) lack of necessary demographic indicators, body measurements, and laboratory indicators; (2) lack of necessary past history and medication history; (3) malignant tumors and other serious diseases of the organ system; (4) metal stents or pacemakers placed in the body, which could not be analyzed by body composition analysis. The study was approved by the Ethics Committee of Beijing You'an Hospital affiliated with Capital Medical University (Jing You Ke Lun Zi [2024] No. 008), and all the enrolled patients signed an informed consent form.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Research methods\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 General condition and body measurements\u003c/h2\u003e \u003cp\u003eThe patients' age, gender, past medical history, abdominal ultrasound and other clinical data were collected, and arrangements were made to measure their height, body mass, waist circumference, hip circumference, and BMI, ASM, body fat rate, visceral fat area, basal metabolic rate and other indexes by using Inbody720 Body Composition Tester (Biospace, Korea).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Laboratory examination\u003c/h2\u003e \u003cp\u003eVenous blood was drawn early in the morning after 12h of fasting, and liver and kidney functions, blood lipids, blood glucose, fasting insulin and other indexes were measured, and the homeostasis model assessment of insulin resistance (HOMA-IR) was calculated, with HOMA-IR\u0026thinsp;=\u0026thinsp;fasting glucose (mmol/L) \u0026times; fasting insulin (mmol/L). /L) \u0026times; fasting insulin (\u0026micro;U/mL)/22.5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Diagnosis of MAFLD\u003c/h2\u003e \u003cp\u003eBased on radiologic diagnosis of hepatic steatosis and the presence of any of the following three diseases\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e12\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e: 1) overweight or obesity: 1) BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m2; 2) the presence of diabetes mellitus; and 3) metabolic dysregulation. Metabolic disorders are defined as two or more of the following:\u003c/p\u003e \u003cp\u003e① Waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;102cm for men and \u0026ge;\u0026thinsp;88cm for women;\u003c/p\u003e \u003cp\u003e②Blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;130/85mmHg or receiving antihypertensive treatment;\u003c/p\u003e \u003cp\u003e③ Triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;1.7mmol/L or undergoing lipid-lowering treatment;\u003c/p\u003e \u003cp\u003e④Pre-diabetes: fasting blood glucose level of 100-125mg/dL or glycated blood glucose protein 5.7\u0026ndash;6.4;\u003c/p\u003e \u003cp\u003e⑤ HDL-C: male\u0026thinsp;\u0026lt;\u0026thinsp;1.0 mmol/L; female\u0026thinsp;\u0026lt;\u0026thinsp;1.3 mmol/L;\u003c/p\u003e \u003cp\u003e⑥ HOMA-IR\u0026thinsp;\u0026ge;\u0026thinsp;2.5;\u003c/p\u003e \u003cp\u003e⑦C-reactive protein level\u0026thinsp;\u0026gt;\u0026thinsp;2mg/L.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Physical Activity Scale for Elderly Questionnaire\u003c/h2\u003e \u003cp\u003eThe PASE (Physical Activity Scale for Elderly) questionnaire was used to investigate the physical activity level of the elderly in the Beijing community, which is a classic international epidemiological questionnaire on physical activity for the elderly. The PASE questionnaire consists of 10 questions and 26 issues, including leisure-related physical activity, housework-related physical activity and occupation-related physical activity. The questionnaire was used to investigate the physical activities engaged in by the respondents in the past 7 days, and the score weights and scores of different test questions were used to calculate the final physical activity scores of each item, and the total PASE scores were in the range of 0-400 points. The reliability and validity of the modified PASE questionnaire have been verified\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e13\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical methods\u003c/h2\u003e \u003cp\u003eSPSS 26.0 was used to analyze the data statistically. Measurement information conforming to normal distribution was described by mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x\u0026thinsp;\u0026plusmn;\u0026thinsp;s), and independent samples t-test was used for comparison between two groups; non-normally distributed measurement information was described by interquartile spacing method M(P25,P75), and Mann-WhitneyU rank sum test was used for comparison between two groups. Binary logistic regression was used to analyze the correlation between type and level of physical activity and sleep status on the prevalence of metabolism-related fatty liver disease with a test level of α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline Characteristics\u003c/h2\u003e \u003cp\u003eA total of 815 elderly individuals aged 65 years and older (mean age: 70.48\u0026thinsp;\u0026plusmn;\u0026thinsp;4.95 years) were recruited for this study, including 396 patients with metabolic-associated fatty liver disease (MAFLD) and 419 non-MAFLD subjects, resulting in a MAFLD prevalence rate of 48.58%. Among the MAFLD patients, 88 (22.2%) were male, and 308 (77.8%) were female, indicating a higher prevalence of MAFLD among females compared to males. Significant differences were observed between the MAFLD and non-MAFLD groups in terms of age, gender, body mass index (BMI), and smoking history (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, a higher BMI was associated with an increased likelihood of MAFLD. However, no significant differences were found between the two groups regarding the prevalence of hypertension, diabetes mellitus, or coronary heart disease (\u003cem\u003eP\u0026thinsp;\u0026gt;\u003c/em\u003e\u0026thinsp;0.05). Further details are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eComparison of general information between MAFLD and non-MAFLD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMAFLD(n\u0026thinsp;=\u0026thinsp;396)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-MAFLD(n\u0026thinsp;=\u0026thinsp;419)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e∕t\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.04\u0026thinsp;\u0026plusmn;\u0026thinsp;4.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.90\u0026thinsp;\u0026plusmn;\u0026thinsp;4.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=-2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.99\u0026thinsp;\u0026plusmn;\u0026thinsp;3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.74\u0026thinsp;\u0026plusmn;\u0026thinsp;2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;10.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"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\u003eHeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160.29\u0026thinsp;\u0026plusmn;\u0026thinsp;7.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159.24\u0026thinsp;\u0026plusmn;\u0026thinsp;7.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=-1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.18\u0026thinsp;\u0026plusmn;\u0026thinsp;10.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.94\u0026thinsp;\u0026plusmn;\u0026thinsp;10.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=-0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=-7.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"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\u003eLDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=-1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=-3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"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\u003eGender\u003c/p\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189\u003c/p\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u0026thinsp;=\u0026thinsp;47.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003cp\u003elliterate education\u003c/p\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003cp\u003eSecondary education\u003c/p\u003e \u003cp\u003eachelor degree\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\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eZ\u0026thinsp;=\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ehigh blood pressure\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\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\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eZ\u0026thinsp;=\u0026thinsp;1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\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\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eZ\u0026thinsp;=\u0026thinsp;1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e263\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ecoronary heart disease\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\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\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eZ\u0026thinsp;=\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSmoked at least 100 cigarettes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\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\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eZ\u0026thinsp;=\u0026thinsp;34.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e283\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\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates a significant difference, denoted by\u003csup\u003e*\u003c/sup\u003e, while \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 indicates a very significant difference, denoted by \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Correlation between different types of physical activity and prevalence of MAFLD\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of the binary logistic regression analysis, examining the relationship between different types of physical activity (PA) and the prevalence of metabolic-associated fatty liver disease (MAFLD) in the elderly population. The findings indicate a significant negative correlation between total PA, leisure-related PA, and occupational PA levels and the prevalence of MAFLD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjusting for age, gender, BMI, and other confounding variables, Models 2 and 3 confirmed that this association remained statistically significant.Additionally, an inverse correlation was observed between housework-related PA and MAFLD prevalence in the unadjusted model (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, after adjusting for confounding variables, Model 3 showed that this association was no longer statistically significant (\u003cem\u003eP\u0026thinsp;\u0026gt;\u003c/em\u003e\u0026thinsp;0.05).\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\u003eBinary logistic regression results of different types of physical activity and prevalence of MAFLD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal PA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.98\u0026thinsp;~\u0026thinsp;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.98\u0026thinsp;~\u0026thinsp;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99(0.99\u0026thinsp;~\u0026thinsp;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeisure-time PA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.98\u0026thinsp;~\u0026thinsp;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.98\u0026thinsp;~\u0026thinsp;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.98 (0.98\u0026thinsp;~\u0026thinsp;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold time-PA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.98\u0026thinsp;~\u0026thinsp;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.040\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.97\u0026thinsp;~\u0026thinsp;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99 (0.98\u0026thinsp;~\u0026thinsp;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation time-PA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.98\u0026thinsp;~\u0026thinsp;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.98\u0026thinsp;~\u0026thinsp;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.98 (0.97\u0026thinsp;~\u0026thinsp;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\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\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates a significant difference, denoted by\u003csup\u003e*\u003c/sup\u003e, while \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 indicates a very significant difference, denoted by \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eModel 1: Crude; Model 2: Adjust: age, gender; Model 3: Adjust: Age, Gender, BMI, triglycerides, totalcholesterol, ALT, AST, AST/ALT, hypertension, coronary heart disease, diabetes, stroke, ASCVD, dyslipidemia, metabolic syndrome.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Correlation between different physical activity levels and prevalence of MAFLD\u003c/h2\u003e \u003cp\u003eThe total physical activity of the elderly population was further categorized into four levels\u0026mdash;light physical activity (LPA), moderate physical activity (MPA), moderate-to-vigorous physical activity (MVPA), and vigorous physical activity (VPA)\u0026mdash;based on quartiles. Regression analysis revealed that MPA was not significantly associated with the risk of MAFLD (OR: 0.71, 95% CI: 0.38\u0026ndash;1.32). In contrast, both MVPA (OR: 0.24, 95% CI: 0.14\u0026ndash;1.41) and VPA (OR: 0.05, 95% CI: 0.03\u0026ndash;0.09) demonstrated a significant negative correlation with MAFLD prevalence.Furthermore, after adjusting for age, gender, and BMI, Models II and III continued to show a highly significant association (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), reinforcing the robustness of these findings.\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\u003eBinary logistic regression results of different levels of physical strength and prevalence of MAFLD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.71 (0.38\u0026thinsp;~\u0026thinsp;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71 (0.38\u0026thinsp;~\u0026thinsp;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.59 (0.29\u0026thinsp;~\u0026thinsp;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.24 (0.14\u0026thinsp;~\u0026thinsp;0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24 (0.14\u0026thinsp;~\u0026thinsp;0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27 (0.14\u0026thinsp;~\u0026thinsp;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05 (0.03\u0026thinsp;~\u0026thinsp;0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05 (0.03\u0026thinsp;~\u0026thinsp;0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05 (0.03\u0026thinsp;~\u0026thinsp;0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\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\u003eLPA,light physical activity; MPA, moderate physical activity; MVPA, moderate-to-vigorous physical activity; VPA, vigorous physical activity.\u003c/p\u003e \u003cp\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates a significant difference, denoted by*, while \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 indicates a very significant difference, denoted by **\u003c/p\u003e \u003cp\u003eModel 1:Crude;Model 2:Adjust: age, gender;Model 3:Adjust:Age,Gender,BMI,triglycerides,totalcholesterol,ALT,AST,AST/ALT,hypertension,coronary heart disease, diabetes, stroke,ASCVD, dyslipidemia, metabolic syndrome\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Correlation between different levels of leisure/occupation-related PA and MAFLD prevalence\u003c/h2\u003e \u003cp\u003eGiven the observed negative correlation between total leisure-related physical activity levels and the risk of MAFLD, a further regression analysis was conducted by categorizing leisure-related physical activity into four levels: LeisurePA-LPA, LeisurePA-MPA, LeisurePA-MVPA, and LeisurePA-VPA. The results indicated a significant negative association between LeisurePA-MPA and MAFLD prevalence (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared to LeisurePA-LPA, LeisurePA-MPA demonstrated a stronger inverse correlation with MAFLD risk, with a statistically significant difference (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, both LeisurePA-MVPA and LeisurePA-VPA exhibited a highly significant negative correlation with MAFLD prevalence (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eOccupation-related physical activity was categorized into four levels: Occupation-LPA, Occupation-MPA, Occupation-MVPA, and Occupation-VPA. Binary logistic regression analysis showed no significant association between Occupation-LPA and MAFLD prevalence (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). A significant correlation was observed between Occupation-MVPA and MAFLD risk (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); however, after adjusting for age and gender, Model 2 indicated that this association was no longer significant. In contrast, Occupation-VPA remained highly significantly associated with a reduced risk of MAFLD across all three models (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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\u003eResults of binary logistic regression of leisure-related different PA levels and prevalence of MAFLD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeisure PA-LPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeisurePA-MPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.42 (0.21\u0026thinsp;~\u0026thinsp;0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38 (0.17\u0026thinsp;~\u0026thinsp;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.38 (0.17\u0026thinsp;~\u0026thinsp;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeisurePA-MVPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.24 (0.11\u0026thinsp;~\u0026thinsp;0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24 (0.10\u0026thinsp;~\u0026thinsp;0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.24 (0.10\u0026thinsp;~\u0026thinsp;0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeisure-VPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07 (0.04\u0026thinsp;~\u0026thinsp;0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07 (0.03\u0026thinsp;~\u0026thinsp;0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07 (0.03\u0026thinsp;~\u0026thinsp;0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e**\u003c/sup\u003e\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\u003eLPA,light physical activity; MPA, moderate physical activity; MVPA, moderate-to-vigorous physical activity; VPA, vigorous physical activity.\u003c/p\u003e \u003cp\u003e \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05 indicates a significant difference, denoted by\u003csup\u003e*\u003c/sup\u003e, while P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 indicates a very significant difference, denoted by \u003csup\u003e**\u003c/sup\u003e\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\u003eResults of binary logistic regression of occupation-related different levels of physical activity and risk of MAFLD prevalence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eoccupation-LPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeisurePA-MPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79(0.60\u0026thinsp;~\u0026thinsp;0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88(0.54\u0026thinsp;~\u0026thinsp;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88(0.54\u0026thinsp;~\u0026thinsp;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eoccupation-MVPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.66 (0.45\u0026thinsp;~\u0026thinsp;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.72 (0.43\u0026thinsp;~\u0026thinsp;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72 (0.43\u0026thinsp;~\u0026thinsp;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eoccupation-VPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57 (0.40\u0026thinsp;~\u0026thinsp;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47 (0.28\u0026thinsp;~\u0026thinsp;0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.47 (0.28\u0026thinsp;~\u0026thinsp;0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003csup\u003e**\u003c/sup\u003e\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\u003eLPA,light physical activity; MPA, moderate physical activity; MVPA, moderate-to-vigorous physical activity; VPA, vigorous physical activity\u003c/p\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates a significant difference, denoted by\u003csup\u003e*\u003c/sup\u003e, while P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 indicates a very significant difference, denoted by \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Relationship between sleep duration as well as sleep quality and MAFLD\u003c/h2\u003e \u003cp\u003eThis study examined the relationship between sleep duration, sleep disorders, and the risk of MAFLD prevalence in the elderly population. The findings indicated no significant association between sleep duration within the range of 6\u0026ndash;10 hours and MAFLD prevalence when compared to a sleep duration of less than 6 hours (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).Similarly, correlation analysis between sleep disorders and MAFLD prevalence revealed no significant associations between the absence of sleep disorders, difficulty falling asleep, early wakefulness, frequent dreaming, or sleepwalking and the risk of MAFLD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\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\u003eBinary logistic regression results of sleep duration and risk of MAFLD prevalence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6-10h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78 (0.57\u0026thinsp;~\u0026thinsp;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78 (0.57\u0026thinsp;~\u0026thinsp;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87 (0.60\u0026thinsp;~\u0026thinsp;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 1:Crude;Model 2:Adjust: age, gender;Model 3:Adjust:Age,Gender,BMI,triglycerides,totalcholesterol,ALT,AST,AST/ALT,hypertension,coronary heart disease, diabetes, stroke,ASCVD, dyslipidemia, metabolic syndrome\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBinary logistic regression results of sleep disorders and risk of MAFLD prevalence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.34 (0.94\u0026thinsp;~\u0026thinsp;1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34 (0.94\u0026thinsp;~\u0026thinsp;1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00 (0.00\u0026thinsp;~\u0026thinsp;Inf)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.43 (0.63\u0026thinsp;~\u0026thinsp;3.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.43 (0.63\u0026thinsp;~\u0026thinsp;3.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00 (0.00\u0026thinsp;~\u0026thinsp;Inf)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.58 (0.19\u0026thinsp;~\u0026thinsp;1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58 (0.19\u0026thinsp;~\u0026thinsp;1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00 (0.00\u0026thinsp;~\u0026thinsp;Inf)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50 (0.07\u0026thinsp;~\u0026thinsp;3.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49 (0.07\u0026thinsp;~\u0026thinsp;3.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00 (0.00\u0026thinsp;~\u0026thinsp;Inf)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 1:Crude;Model 2:Adjust: age, gender;Model 3:Adjust:Age,Gender,BMI,triglycerides,totalcholesterol,ALT,AST,AST/ALT,hypertension,coronary heart disease, diabetes, stroke,ASCVD, dyslipidemia, metabolic syndrome\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNSD,No sleep disorders;DFA,Difficulty falling asleep;EMA,Early morning awakening FD,Frequent dreaming;SW,Sleepwalking\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn this cross-sectional study, we investigated the relationship between physical activity, sleep, and the prevalence of MAFLD in the elderly population. Several key findings emerged. First, total physical activity, recreational physical activity, and occupational physical activity were all negatively correlated with MAFLD prevalence in older adults.Further analysis of different physical activity levels revealed that both MVPA and vigorous PA were significantly associated with a reduced risk of MAFLD. When physical activity types were further categorized by intensity, a negative correlation was observed between various levels of recreational physical activity, as well as high-intensity occupational physical activity, and MAFLD prevalence.However, regarding sleep, neither sleep duration nor different types of sleep disorders were found to be significantly associated with MAFLD prevalence.\u003c/p\u003e \u003cp\u003ePhysical activity and sleep are widely recognized as key lifestyle factors influencing the risk of MAFLD\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e14\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. A growing body of epidemiological research has demonstrated a strong correlation between physical inactivity and adverse health outcomes, including an increased risk of MAFLD and cardiovascular disease\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e15\u003c/sup\u003e\u003csup\u003e][\u003c/sup\u003e\u003csup\u003e16\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Regular physical activity plays a crucial role in maintaining metabolic health, supporting cardiovascular function, and regulating systemic inflammation\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e17\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.However, in recent years, global physical inactivity has risen significantly, with more than 25% of the population reportedly failing to meet the World Health Organization\u0026rsquo;s recommended physical activity levels\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e18\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Epidemiological studies have consistently linked physical inactivity to chronic conditions such as obesity, metabolic syndrome, and MAFLD\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e19\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Increasing physical activity levels has been identified as an effective intervention for improving MAFLD outcomes\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e20\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. A systematic review and meta-analysis by Zelber-Sagi et al. reported that higher levels of physical activity were associated with a significantly lower risk of developing MAFLD\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e21\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Moreover, regular moderate-to-high-intensity physical activity was shown to reduce hepatic steatosis and lower the risk of hepatic inflammation and fibrosis\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e22\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.Sleep also plays a critical role in health, as individuals spend approximately one-third of their lives asleep. High-quality sleep is essential for cardiovascular health and the regulation of endocrine and immune functions. However, in recent decades, the prevalence of short sleep duration (defined as \u0026lt;\u0026thinsp;6 hours) has exceeded 20%\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e23\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Epidemiological studies have demonstrated associations between insufficient sleep duration and conditions such as obesity, metabolic syndrome, and cardiovascular disease\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e24\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Furthermore, research has identified short sleep duration as a potential risk factor for MAFLD\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e25\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. A meta-analysis by Wijarnpreecha et al., which included six studies, found a significant association between short sleep duration and an increased risk of MAFLD\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e26\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe primary finding of this study confirms that total physical activity levels in elderly individuals serve as a protective factor against MAFLD. Furthermore, the prevalence of MAFLD in this population was significantly associated with leisure and occupational physical activity. These results align with previous research.Seungho Ryu conducted a cross-sectional study on the association between sedentary time, physical activity levels, and nonalcoholic fatty liver disease (NAFLD) in a cohort of 139,056 South Koreans\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e27\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. After adjusting for potential confounders, including sedentary time, total calorie intake, smoking, and alcohol consumption, a significant inverse relationship between physical activity levels and MAFLD prevalence remained evident (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings are consistent with those of the present study.Similarly, Donghee Kim investigated the impact of physical activity on NAFLD in a cohort of 24,588 middle-aged adults in the United States (mean age: 47 years)\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e28\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. The study found that both recreational and transportation-related physical activity had a significant protective effect against NAFLD, whereas occupational physical activity showed no significant association with MAFLD. This variation may be attributed to differences in occupational activity levels across age groups.In older adults, the health benefits of light physical activity are more pronounced due to lower metabolic efficiency. Aging leads to a decreased basal metabolic rate, making even light-to-moderate occupational activity effective in improving energy balance and reducing visceral fat accumulation, thereby lowering MAFLD prevalence. In contrast, middle-aged adults, who have a higher basal metabolic rate, require more intensive physical activity to achieve similar health benefits, which may explain why high-intensity occupational activity appears less protective in this group.Moreover, occupational activities among older adults tend to be of low to moderate intensity and moderate duration, such as light labor or supportive work. These activity levels align with health-promoting exercise intensities and contribute to improved insulin sensitivity, enhanced energy metabolism, and better fat distribution, thereby offering protection against MAFLD. Conversely, high-intensity occupational tasks, such as prolonged standing, heavy lifting, and mechanized operations, may increase energy expenditure but can also lead to chronic fatigue, metabolic stress, and inflammatory responses, potentially diminishing their protective effects against MAFLD.This discrepancy underscores the need for targeted intervention strategies that consider the type and characteristics of occupational activities to effectively prevent and manage MAFLD across different age groups.\u003c/p\u003e \u003cp\u003eThis study also found that different levels of leisure-related and occupational-related physical activity were differentially associated with MAFLD prevalence in the elderly population. Compared to low-intensity leisure-related physical activity, moderate-intensity, moderate-to-vigorous-intensity, and high-intensity leisure-related physical activity were all significantly associated with a reduced prevalence of MAFLD. However, when analyzing different levels of occupational-related physical activity, only high-intensity occupational activity demonstrated a significant association with MAFLD prevalence.Chinese scholar Bing Renjie conducted a study using a leisure physical activity questionnaire to assess the activity levels of 1,124 older adults and explored the impact of physical activity on MAFLD prevalence\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e29\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. The findings indicated that increasing leisure-time physical activity significantly reduced the risk of MAFLD in older adults. Similarly, a meta-analysis examining the relationship between physical activity and MAFLD risk reached the same conclusion. The analysis reported that the highest levels of physical activity were associated with a lower risk of MAFLD compared to the lowest levels, with a risk ratio (RR) of 0.82 for every additional 500 MET-minutes of physical activity per week\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e30\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. The study concluded that engaging in at least 500 MET-minutes of physical activity per week may reduce the risk of developing MAFLD.The protective effects of different levels of leisure-related physical activity in reducing MAFLD risk may be attributed to its diverse forms, voluntary nature, psychological benefits, and association with overall healthy lifestyle behaviors. In contrast, only high-intensity occupational physical activity significantly reduced the risk of MAFLD, while low- and moderate-intensity occupational activities did not exhibit similar protective effects. This may be due to insufficient energy expenditure, the repetitive nature of occupational tasks, and potentially negative metabolic effects. These findings suggest that older adults should be encouraged to increase their leisure-time physical activity to effectively reduce the risk of MAFLD.\u003c/p\u003e \u003cp\u003eIn addition, this study did not find a significant correlation between sleep duration, sleep quality, and MAFLD in older adults. Previous research has suggested that sleep quality may be a risk factor for MAFLD.A cross-sectional study involving 4,828 participants examined the association between sleep quality and MAFLD after adjusting for age, weight, smoking history, and physical activity. The study found that sleep quality was associated with MAFLD prevalence, with notable gender differences\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e31\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Similarly, Um Y. J. conducted a four-year cohort study investigating the relationship between sleep duration, sleep quality, and MAFLD prevalence\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e32\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. This study evaluated sleep patterns in 143,306 Korean adults without MAFLD (mean age: 36.6 years) and followed them for an average of four years. The findings indicated that short sleep duration was independently associated with an increased risk of developing MAFLD, suggesting that sleep deprivation contributes to both the risk and severity of the disease.However, it is important to consider that sleep duration and quality generally decline with age. Older adults often experience increased sleep fragmentation and reduced deep sleep, which may minimize individual differences and obscure the impact of sleep on MAFLD. Additionally, older adults may compensate for insufficient nighttime sleep through daytime naps or other forms of rest, potentially mitigating the negative effects of poor sleep on metabolic health.\u003c/p\u003e \u003cp\u003ePhysical activity plays a crucial role in the prevention and management of MAFLD through multiple mechanisms, including energy expenditure, insulin resistance, inflammation, and oxidative stress. Regular physical activity enhances energy expenditure, promotes fat oxidation and metabolism, and reduces body fat accumulation, thereby lowering the risk of MAFLD\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e33\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Additionally, physical activity serves as an effective therapeutic approach for individuals with MAFLD. Studies have shown that a 5\u0026ndash;10% reduction in body weight can significantly improve hepatic steatosis and inflammation, while a weight loss exceeding 10% can reduce hepatic fibrosis.Moreover, research suggests that improvements in lipid metabolism due to exercise are independent of body weight and play a key role in reducing hepatic lipid deposition\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e34\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Romero et al. found that regular physical activity, even without changes in body weight, led to reductions in hepatic fat, improved serum liver enzyme levels, and enhanced hepatic fatty acid oxidation\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e35\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Another mechanism by which physical activity benefits individuals with MAFLD is its ability to reduce systemic inflammation. This effect is partially attributed to muscle-derived factors, including cytokines and other peptides secreted by muscle fibers, which exert paracrine and endocrine functions. These substances, released in response to muscle contractions, may have both direct anti-inflammatory effects and indirect effects on fat metabolism, ultimately reducing the risk of MAFLD.In contrast, the mechanisms underlying the relationship between sleep disorders and MAFLD prevalence remain unclear. It has been suggested that the hypothalamic-pituitary-adrenal (HPA) axis and autonomic nervous system activity play a vital role in regulating immune and cardiometabolic functions\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e36\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Poor sleep quality activates the HPA axis, leading to increased secretion of stress hormones such as cortisol and catecholamines, which may contribute to a higher risk of metabolic syndrome. Additionally, insufficient sleep duration has been shown to increase appetite by elevating ghrelin (the hunger hormone) and reducing leptin levels, ultimately leading to weight gain and obesity\u0026mdash;both of which are risk factors for MAFLD. Furthermore, sleep deprivation has been linked to impaired insulin sensitivity\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e37\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, with insulin resistance being a key factor in the pathogenesis of MAFLD.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, as it employed a cross-sectional design, it was not possible to establish a causal relationship between physical activity, sleep, and MAFLD. Second, due to the large sample size, physical activity levels in the elderly population were assessed using self-reported questionnaires rather than accelerometers, which may have introduced measurement bias due to the subjective nature of the responses. Lastly, although the study adjusted for multiple potential confounders, unaccounted variables such as dietary habits and genetic predisposition may also influence MAFLD risk.\u003c/p\u003e \u003cp\u003eIn conclusion, this study found that moderate-to-high-intensity physical activity was significantly associated with a lower risk of MAFLD, while poorer sleep quality (e.g., difficulty falling asleep and excessive dreaming) was linked to a higher risk. These findings suggest that increasing physical activity levels and improving sleep quality may help reduce MAFLD risk in older adults.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.M.M., Q.Y.S., and Z.X.B. wrote the main manuscript text.\u003cbr\u003eD.L.M., Z.H.Y., and J.H.Y. collected the data.\u003cbr\u003eW.Q. and Y.R.X. revised and edited the manuscript.\u003cbr\u003eW.Z., W.J., and Z.J. organized and processed the data.\u003c/p\u003e\n\u003cp\u003eAll authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of the Ethics Committee of Beijing You\u0026apos;an Hospital affiliated with Capital Medical University (Jing You Ke Lun Zi [2024] No. 008).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eLiJ,ZouB,YeoYH,etal.Prevalence,incidence,and outcome of non-alcoholic fatty liver disease in Asia,1999\u0026ndash;2019:a system aticre view and Meta-analysis[J]. Lancet Gastroenterol Hepatol,2019,4(5):389\u0026ndash;398.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eYounossi, Zobair M., Markos Kalligeros, and Linda Henry. \u0026quot;Epidemiology of Metabolic Dysfunction Associated Steatotic Liver Disease.\u0026quot;\u0026nbsp;Clinical and Molecular Hepatology\u0026nbsp;(2024).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eEslam, Mohammed, et al. \u0026quot;Metabolic (dysfunction)-associated fatty liver disease in individuals of normal weight.\u0026quot; Nature reviews Gastroenterology \u0026amp; hepatology 19.10 (2022): 638\u0026ndash;651.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eAdams, L.A.; Anstee, Q.M.; Tilg, H.; Targher, G. Non-alcoholic fatty liver disease and its relationship with cardiovascular disease and other extrahepatic diseases. Gut 2017, 66, 1138\u0026ndash;1153.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eYounossi, Z.M. Non-alcoholic fatty liver disease\u0026mdash;A global public health perspective. J. Hepatol. 2019, 70, 531\u0026ndash;544.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eUmbro, I.; Baratta, F.; Angelico, F.; Del Ben, M. Nonalcoholic fatty liver disease and the kidney: A review. Biomedicines 2021,9, 1370.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eChalasani, N.; Younossi, Z.; Lavine, J.E.; Charlton, M.; Cusi, K.; Rinella, M.; Harrison, S.A.; Brunt, E.M.; Sanyal, A.J. The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases. Hepatology 2018, 67, 328\u0026ndash;357.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDyson, J.K.; Anstee, Q.M.; McPherson, S. Non-alcoholic fatty liver disease: A practical approach to treatment. Frontline Gastroenterol. 2014, 5, 277\u0026ndash;286.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eAnguloP,HuiJM,MarchesiniG,etal.TheMAFLDfibrosis score:A noninvasive system that identifies liver fibrosisin patients with MAFLD.Hepatology,2007,45(4):846\u0026ndash;854.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLavi, Einav Sheinman.\u0026nbsp;The Association of Physical Activity With the Incidence and Progression of NAFLD and Liver Fibrosis. MS thesis. University of Haifa (Israel), 2023.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKim, Donghee, et al. \u0026quot;Physical activity, measured objectively, is associated with lower mortality in patients with nonalcoholic fatty liver disease.\u0026quot;\u0026nbsp;Clinical Gastroenterology and Hepatology\u0026nbsp;19.6 (2021): 1240\u0026ndash;1247.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLiu, Qiling, et al. \u0026quot;A comparison of NAFLD and MAFLD diagnostic criteria in contemporary urban healthy adults in China: a cross-sectional study.\u0026quot;\u0026nbsp;BMC gastroenterology\u0026nbsp;22.1 (2022): 471.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eYu Hongjun, Qiu Jun. Reliability and validity of the Physical Activity Scale for the Elderly (PASE) in measuring physical activity among Chinese older adults [J]. Journal of Shanghai University of Sport, 2014, 38(5): 45.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eIrwin, M.R.; Olmstead, R.; Carroll, J.E. Sleep disturbance, sleep duration, and inflammation: A Systematic review and meta analysis of cohort studies and experimental sleep deprivation. Biol. Psychiatry 2016, 80, 40\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGrandner, M.A.; Sands-Lincoln, M.R.; Pak, V.M.; Garland, S.N. Sleep duration, cardiovascular disease, and proinflammatory biomarkers. Nat. Sci. Sleep 2013, 5, 93\u0026ndash;107.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSheehan, C.M.; Frochen, S.E.; Walsemann, K.M.; Ailshire, J.A. Are U.S. adults reporting less sleep?: Findings from sleep duration trends in the National Health Interview Survey, 2004\u0026ndash;2017. Sleep 2019, 42, zsy221.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eWarburton, D.E.R.; Nicol, C.W.; Bredin, S.S.D. Health benefits of physical activity: The evidence. CMAJ 2006, 174, 801\u0026ndash;809.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGuthold, R.; Stevens, G.A.; Riley, L.M.; Bull, F.C. Worldwide trends in insufficient physical activity from 2001 to 2016: A pooled analysis of 358 population-based surveys with 1.9\u0026nbsp;million participants. Lancet Glob. Health 2018, 6, e1077-e1086.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDonnelly, J.E.; Blair, S.N.; Jakicic, J.M.; Manore, M.M.; Rankin, J.W.; Smith, B.K. Appropriate physical activity intervention strategies for weight loss and prevention of weight regain for adults. Med. Sci. Sports Exerc. 2009, 41, 459\u0026ndash;471\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eJia Guoyu. Randomized controlled study on the intervention effects of different exercise modalities on non-alcoholic fatty liver disease [D]. Tianjin Medical University, 2021.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eZelber-Sagi, S.; Nitzan-Kaluski, D.; Goldsmith, R.; et al. Role of physical activity in nonalcoholic fatty liver disease: A systematic review and meta-analysis. Hepatology 2007, 48, 1794\u0026ndash;1805.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eXi, B.; He, D.; Zhang, M.; Xue, J.; Zhou, D. Short sleep duration predicts risk of metabolic syndrome: A systematic review and meta-analysis. Sleep Med. Rev. 2014, 18, 293\u0026ndash;297.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKim CW༌Yun K༌Jung HS༌et al༎ Sleep duration and quality in relation to non ། alcoholic fatty liver disease in middle ། aged workers and their spouses༻J༽༎ Journal of Hepatology༌2013༌59(2) : 351 ། 357༎\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eRinella, M.E. Nonalcoholic fatty liver disease: A systematic review. JAMA 2015, 313, 2263\u0026ndash;2273.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLiu Lifeng. Association analysis between sleep duration and metabolically associated fatty liver disease: A cross-sectional study [D]. Huazhong University of Science and Technology, Wuhan, 2014.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eWijarnpreecha K༌Thongprayoon C༌Panjawatanan P༌et al༎Short sleep duration and risk of nonalcoholic fatty liver disease: Asystematic review and meta།analysis༻J༽༎Journal of Gastroenterology and Hepatology༌2016༌31( 11) : 1802 ། 1807༎\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSlavish, D.C.; Taylor, D.J.; Lichstein, K.L. Intraindividual variability in sleep and comorbid medical and mental health conditions.Sleep 2019, 42, zsz052.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKim, Donghee, et al. \u0026quot;Inadequate physical activity and sedentary behavior are independent predictors of nonalcoholic fatty liver disease.\u0026quot;\u0026nbsp;Hepatology\u0026nbsp;72.5 (2020): 1556\u0026ndash;1568.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBing Renjie, Wang Yubo, Zhou Kaixiang, Bao Dapeng. A cross-sectional study on the relationship between leisure-time physical activity levels and non-alcoholic fatty liver disease in the elderly population of Tianjin [A]. Proceedings of the 13th National Sports Science Conference\u0026mdash;Special Report (Sports Medicine Division) [C]. Chinese Society of Sports Science, 2023: 3.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBose, M.; Olivan, B.; Laferrere, B. Stress and obesity: The role of the hypothalamic-pituitary-adrenal axis in metabolic disease.Curr. Opin. Endocrinol. Diabetes Obes. 2009, 16, 340\u0026ndash;346.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eTakahashi A, Anzai Y, Kuroda M, et al. Effects of sleep quality on non-alcoholic fatty liver disease: a cross-sectional survey[J]. BMJ open, 2020, 10(10).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eUm Y J, Chang Y, Jung H S, et al. Sleep duration, sleep quality, and the development of nonalcoholic fatty liver disease: a cohort study[J]. Clinical and translational gastroenterology, 2021, 12(10).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eE. Teixeira de Lemos et al. \u0026quot;Regular Physical Exercise as a Strategy to Improve Antioxidant and Anti-Inflammatory Status: Benefits in Type 2 Diabetes Mellitus.\u0026quot; Oxidative Medicine and Cellular Longevity, 2012 (2012).\u0026nbsp;\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eHirokazu Takahashi et al. \u0026quot;Therapeutic Approaches to Nonalcoholic Fatty Liver Disease: Exercise Intervention and Related Mechanisms.\u0026quot; Frontiers in Endocrinology, 9 (2018).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eA. Onyango et al. \u0026quot;Cellular Stresses and Stress Responses in the Pathogenesis of Insulin Resistance.\u0026quot;\u0026nbsp;Oxidative Medicine and Cellular Longevity, 2018 (2018).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBuckley, T.M.; Schatzberg, A.F. On the interactions of the hypothalamic-pituitary-adrenal (HPA) axis and sleep: Normal HPA axis activity and circadian rhythm, exemplary sleep disorders. J. Clin. Endocrinol. Metab. 2005, 90, 3106\u0026ndash;3114.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBriancon-Marjollet, A.; Weiszenstein, M.; Henri, M.; Thomas, A.; Godin-Ribuot, D.; Polak, J. The impact of sleep disorders on glucose metabolism: Endocrine and molecular mechanisms. Diabetol. Metab. Syndr. 2015, 7, 25.\u003c/span\u003e\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":"metabolism-associated fatty liver disease, MAFLD, physical activity, sleep","lastPublishedDoi":"10.21203/rs.3.rs-6143971/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6143971/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003ePhysical inactivity has been identified as a potential risk factor for metabolic-associated fatty liver disease (MAFLD) in the elderly. However, the specific effects of different types and intensities of physical activity on MAFLD risk remain unclear. This study aims to examine the correlation between the type and level of physical activity and the prevalence of MAFLD in an elderly community-based population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA cross-sectional study was conducted among 815 older adults aged 65 years and above. Participants' demographic and anthropometric data were collected through field assessments and questionnaires. Body composition was measured using InBody720 (Biospace, Korea), while fatty liver diagnosis was performed via ultrasound, and liver fat content and elasticity were assessed using FibroScan. Physical activity levels were evaluated using the Physical Activity Scale for the Elderly (PASE). Binary logistic regression models were employed to analyze the relationship between various types and intensities of physical activity and MAFLD prevalence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Among the 815 participants, 396 were diagnosed with MAFLD. After adjusting for confounding variables, higher levels of total physical activity (OR: 0.99, 95% CI: 0.98–0.99, P \u0026lt; 0.001), leisure-related physical activity (OR: 0.98, 95% CI: 0.98–0.98, P \u0026lt; 0.001), and occupational-related physical activity (OR: 0.99, 95% CI: 0.98–0.99, P \u0026lt; 0.001) were significantly associated with a lower risk of MAFLD. In contrast, home-related physical activity (OR: 0.99, 95% CI: 0.98–0.99, P = 0.103) showed no significant association.When total physical activity was categorized into four levels—light (LPA), moderate (MPA), moderate-to-vigorous (MVPA), and vigorous (VPA)—MPA was not significantly associated with MAFLD risk (OR: 0.71, 95% CI: 0.38–1.32). However, MVPA (OR: 0.24, 95% CI: 0.14–1.41) and VPA (OR: 0.05, 95% CI: 0.03–0.09) exhibited strong negative associations with MAFLD risk. Specifically, moderate-intensity leisure physical activity (LeisurePA-MPA) was significantly associated with a reduced risk of MAFLD (OR: 0.42, 95% CI: 0.21–0.87, P \u0026lt; 0.05), as were moderate-to-vigorous (LeisurePA-MVPA, OR: 0.24, 95% CI: 0.11–0.50, P \u0026lt; 0.001) and vigorous (LeisurePA-VPA, OR: 0.07, 95% CI: 0.03–0.15, P \u0026lt; 0.001) leisure activities. Additionally, only vigorous-intensity occupational physical activity (Occupation-VPA) was significantly associated with a reduced risk of MAFLD (OR: 0.47, 95% CI: 0.28–0.77, P \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eHigher levels of total, leisure-related, and occupational-related physical activity are significantly associated with a lower risk of MAFLD in older adults, whereas home-related physical activity shows no significant effect. In terms of activity intensity, MVPA and VPA demonstrate strong protective effects against MAFLD, particularly in leisure and occupational settings. These findings suggest that older adults should engage in moderate-to-vigorous intensity leisure and occupational activities to effectively reduce MAFLD risk.\u003c/p\u003e","manuscriptTitle":"Correlation of Physical Activity and Sleep Quality with Metabolic-Associated Fatty Liver Disease in Community-Based Older Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-11 06:23:07","doi":"10.21203/rs.3.rs-6143971/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":"7f997b07-4db0-4e81-84c1-4006ddf55996","owner":[],"postedDate":"March 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-11T06:23:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-11 06:23:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6143971","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6143971","identity":"rs-6143971","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}